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2017


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Editorial for the Special Issue on Microdevices and Microsystems for Cell Manipulation

Hu, W., Ohta, A. T.

8, Multidisciplinary Digital Publishing Institute, September 2017 (misc)

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DOI [BibTex]

2017


DOI [BibTex]


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Physical and Behavioral Factors Improve Robot Hug Quality

Block, A. E., Kuchenbecker, K. J.

Workshop Paper (2 pages) presented at the RO-MAN Workshop on Social Interaction and Multimodal Expression for Socially Intelligent Robots, Lisbon, Portugal, August 2017 (misc)

Abstract
A hug is one of the most basic ways humans can express affection. As hugs are so common, a natural progression of robot development is to have robots one day hug humans as seamlessly as these intimate human-human interactions occur. This project’s purpose is to evaluate human responses to different robot physical characteristics and hugging behaviors. Specifically, we aim to test the hypothesis that a warm, soft, touch-sensitive PR2 humanoid robot can provide humans with satisfying hugs by matching both their hugging pressure and their hugging duration. Thirty participants experienced and evaluated twelve hugs with the robot, divided into three randomly ordered trials that focused on physical robot char- acteristics and nine randomly ordered trials with varied hug pressure and duration. We found that people prefer soft, warm hugs over hard, cold hugs. Furthermore, users prefer hugs that physically squeeze them and release immediately when they are ready for the hug to end.

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Project Page [BibTex]

Project Page [BibTex]


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Crowdshaping Realistic 3D Avatars with Words

Streuber, S., Ramirez, M. Q., Black, M., Zuffi, S., O’Toole, A., Hill, M. Q., Hahn, C. A.

August 2017, Application PCT/EP2017/051954 (misc)

Abstract
A method for generating a body shape, comprising the steps: - receiving one or more linguistic descriptors related to the body shape; - retrieving an association between the one or more linguistic descriptors and a body shape; and - generating the body shape, based on the association.

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Google Patents [BibTex]

Google Patents [BibTex]


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Physically Interactive Exercise Games with a Baxter Robot

Fitter, N. T., Kuchenbecker, K. J.

Hands-on demonstration presented at the IEEE World Haptics Conference (WHC), Munich, Germany, June 2017 (misc)

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Project Page [BibTex]

Project Page [BibTex]


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Proton Pack: Visuo-Haptic Surface Data Recording

Burka, A., Kuchenbecker, K. J.

Hands-on demonstration presented at the IEEE World Haptics Conference (WHC), Munich, Germany, June 2017 (misc)

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Project Page [BibTex]

Project Page [BibTex]


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Teaching a Robot to Collaborate with a Human Via Haptic Teleoperation

Hu, S., Kuchenbecker, K. J.

Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Munich, Germany, June 2017 (misc)

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Project Page [BibTex]

Project Page [BibTex]


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How Should Robots Hug?

Block, A. E., Kuchenbecker, K. J.

Work-in-progress paper (2 pages) presented at the IEEE World Haptics Conference (WHC), Munich, Germany, June 2017 (misc)

hi

Project Page [BibTex]

Project Page [BibTex]


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An Interactive Augmented-Reality Video Training Platform for the da Vinci Surgical System

Carlson, J., Kuchenbecker, K. J.

Workshop paper (3 pages) presented at the ICRA Workshop on C4 Surgical Robots, Singapore, May 2017 (misc)

Abstract
Teleoperated surgical robots such as the Intuitive da Vinci Surgical System facilitate minimally invasive surgeries, which decrease risk to patients. However, these systems can be difficult to learn, and existing training curricula on surgical simulators do not offer students the realistic experience of a full operation. This paper presents an augmented-reality video training platform for the da Vinci that will allow trainees to rehearse any surgery recorded by an expert. While the trainee operates a da Vinci in free space, they see their own instruments overlaid on the expert video. Tools are identified in the source videos via color segmentation and kernelized correlation filter tracking, and their depth is calculated from the da Vinci’s stereoscopic video feed. The user tries to follow the expert’s movements, and if any of their tools venture too far away, the system provides instantaneous visual feedback and pauses to allow the user to correct their motion. The trainee can also rewind the expert video by bringing either da Vinci tool very close to the camera. This combined and augmented video provides the user with an immersive and interactive training experience.

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[BibTex]

[BibTex]


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Hand-Clapping Games with a Baxter Robot

Fitter, N. T., Kuchenbecker, K. J.

Hands-on demonstration presented at ACM/IEEE International Conference on Human-Robot Interaction (HRI), Vienna, Austria, March 2017 (misc)

Abstract
Robots that work alongside humans might be more effective if they could forge a strong social bond with their human partners. Hand-clapping games and other forms of rhythmic social-physical interaction may foster human-robot teamwork, but the design of such interactions has scarcely been explored. At the HRI 2017 conference, we will showcase several such interactions taken from our recent work with the Rethink Robotics Baxter Research Robot, including tempo-matching, Simon says, and Pat-a-cake-like games. We believe conference attendees will be both entertained and intrigued by this novel demonstration of social-physical HRI.

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Project Page [BibTex]

Project Page [BibTex]


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Automatic OSATS Rating of Trainee Skill at a Pediatric Laparoscopic Suturing Task

Oquendo, Y. A., Riddle, E. W., Hiller, D., Blinman, T. A., Kuchenbecker, K. J.

Surgical Endoscopy, 31(Supplement 1):S28, Extended abstract presented as a podium presentation at the Annual Meeting of the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES), Springer, Houston, USA, March 2017 (misc)

Abstract
Introduction: Minimally invasive surgery has revolutionized surgical practice, but challenges remain. Trainees must acquire complex technical skills while minimizing patient risk, and surgeons must maintain their skills for rare procedures. These challenges are magnified in pediatric surgery due to the smaller spaces, finer tissue, and relative dearth of both inanimate and virtual simulators. To build technical expertise, trainees need opportunities for deliberate practice with specific performance feedback, which is typically provided via tedious human grading. This study aimed to validate a novel motion-tracking system and machine learning algorithm for automatically evaluating trainee performance on a pediatric laparoscopic suturing task using a 1–5 OSATS Overall Skill rating. Methods: Subjects (n=14) ranging from medical students to fellows per- formed one or two trials of an intracorporeal suturing task in a custom pediatric laparoscopy training box (Fig. 1) after watching a video of ideal performance by an expert. The position and orientation of the tools and endoscope were recorded over time using Ascension trakSTAR magnetic motion-tracking sensors, and both instrument grasp angles were recorded over time using flex sensors on the handles. The 27 trials were video-recorded and scored on the OSATS scale by a senior fellow; ratings ranged from 1 to 4. The raw motion data from each trial was processed to calculate over 200 preliminary motion parameters. Regularized least-squares regression (LASSO) was used to identify the most predictive parameters for inclusion in a regression tree. Model performance was evaluated by leave-one-subject-out cross validation, wherein the automatic scores given to each subject’s trials (by a model trained on all other data) are compared to the corresponding human rater scores. Results: The best-performing LASSO algorithm identified 14 predictive parameters for inclusion in the regression tree, including completion time, linear path length, angular path length, angular acceleration, grasp velocity, and grasp acceleration. The final model’s raw output showed a strong positive correlation of 0.87 with the reviewer-generated scores, and rounding the output to the nearest integer yielded a leave-one-subject-out cross-validation accuracy of 77.8%. Results are summarized in the confusion matrix (Table 1). Conclusions: Our novel motion-tracking system and regression model automatically gave previously unseen trials overall skill scores that closely match scores from an expert human rater. With additional data and further development, this system may enable creation of a motion-based training platform for pediatric laparoscopic surgery and could yield insights into the fundamental components of surgical skill.

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[BibTex]

[BibTex]


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How Much Haptic Surface Data is Enough?

Burka, A., Kuchenbecker, K. J.

Workshop paper (5 pages) presented at the AAAI Spring Symposium on Interactive Multi-Sensory Object Perception for Embodied Agents, Stanford, USA, March 2017 (misc)

Abstract
The Proton Pack is a portable visuo-haptic surface interaction recording device that will be used to collect a vast multimodal dataset, intended for robots to use as part of an approach to understanding the world around them. In order to collect a useful dataset, we want to pick a suitable interaction duration for each surface, noting the tradeoff between data collection resources and completeness of data. One interesting approach frames the data collection process as an online learning problem, building an incremental surface model and using that model to decide when there is enough data. Here we examine how to do such online surface modeling and when to stop collecting data, using kinetic friction as a first domain in which to apply online modeling.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]

2014


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Model transport: towards scalable transfer learning on manifolds - supplemental material

Freifeld, O., Hauberg, S., Black, M. J.

(9), April 2014 (techreport)

Abstract
This technical report is complementary to "Model Transport: Towards Scalable Transfer Learning on Manifolds" and contains proofs, explanation of the attached video (visualization of bases from the body shape experiments), and high-resolution images of select results of individual reconstructions from the shape experiments. It is identical to the supplemental mate- rial submitted to the Conference on Computer Vision and Pattern Recognition (CVPR 2014) on November 2013.

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PDF [BibTex]


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Local Gaussian Regression

Meier, F., Hennig, P., Schaal, S.

arXiv preprint, March 2014, clmc (misc)

Abstract
Abstract: Locally weighted regression was created as a nonparametric learning method that is computationally efficient, can learn from very large amounts of data and add data incrementally. An interesting feature of locally weighted regression is that it can work with ...

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Web link (url) [BibTex]

Web link (url) [BibTex]


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Fibrillar structures to reduce viscous drag on aerodynamic and hydrodynamic wall surfaces

Castillo, L., Aksak, B., Sitti, M.

March 2014, US Patent App. 14/774,767 (misc)

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[BibTex]

[BibTex]


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The design of microfibers with mushroom-shaped tips for optimal adhesion

Sitti, M., Aksak, B.

February 2014, US Patent App. 14/766,561 (misc)

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[BibTex]

[BibTex]

2010


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Computationally efficient algorithms for statistical image processing: Implementation in R

Langovoy, M., Wittich, O.

(2010-053), EURANDOM, Technische Universiteit Eindhoven, December 2010 (techreport)

Abstract
In the series of our earlier papers on the subject, we proposed a novel statistical hy- pothesis testing method for detection of objects in noisy images. The method uses results from percolation theory and random graph theory. We developed algorithms that allowed to detect objects of unknown shapes in the presence of nonparametric noise of unknown level and of un- known distribution. No boundary shape constraints were imposed on the objects, only a weak bulk condition for the object's interior was required. Our algorithms have linear complexity and exponential accuracy. In the present paper, we describe an implementation of our nonparametric hypothesis testing method. We provide a program that can be used for statistical experiments in image processing. This program is written in the statistical programming language R.

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PDF [BibTex]

2010


PDF [BibTex]


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Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

Seeger, M., Nickisch, H.

Max Planck Institute for Biological Cybernetics, December 2010 (techreport)

Abstract
We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (Opper&Winther 05) with covariance decoupling techniques (Wipf&Nagarajan 08, Nickisch&Seeger 09), it runs at least an order of magnitude faster than the most commonly used EP solver.

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Web [BibTex]

Web [BibTex]


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A PAC-Bayesian Analysis of Graph Clustering and Pairwise Clustering

Seldin, Y.

Max Planck Institute for Biological Cybernetics, Tübingen, Germany, September 2010 (techreport)

Abstract
We formulate weighted graph clustering as a prediction problem: given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights. This formulation enables practical and theoretical comparison of different approaches to graph clustering as well as comparison of graph clustering with other possible ways to model the graph. We adapt the PAC-Bayesian analysis of co-clustering (Seldin and Tishby, 2008; Seldin, 2009) to derive a PAC-Bayesian generalization bound for graph clustering. The bound shows that graph clustering should optimize a trade-off between empirical data fit and the mutual information that clusters preserve on the graph nodes. A similar trade-off derived from information-theoretic considerations was already shown to produce state-of-the-art results in practice (Slonim et al., 2005; Yom-Tov and Slonim, 2009). This paper supports the empirical evidence by providing a better theoretical foundation, suggesting formal generalization guarantees, and offering a more accurate way to deal with finite sample issues. We derive a bound minimization algorithm and show that it provides good results in real-life problems and that the derived PAC-Bayesian bound is reasonably tight.

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PDF Web [BibTex]

PDF Web [BibTex]


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Sparse nonnegative matrix approximation: new formulations and algorithms

Tandon, R., Sra, S.

(193), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, September 2010 (techreport)

Abstract
We introduce several new formulations for sparse nonnegative matrix approximation. Subsequently, we solve these formulations by developing generic algorithms. Further, to help selecting a particular sparse formulation, we briefly discuss the interpretation of each formulation. Finally, preliminary experiments are presented to illustrate the behavior of our formulations and algorithms.

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PDF [BibTex]

PDF [BibTex]


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Robust nonparametric detection of objects in noisy images

Langovoy, M., Wittich, O.

(2010-049), EURANDOM, Technische Universiteit Eindhoven, September 2010 (techreport)

Abstract
We propose a novel statistical hypothesis testing method for detection of objects in noisy images. The method uses results from percolation theory and random graph theory. We present an algorithm that allows to detect objects of unknown shapes in the presence of nonparametric noise of unknown level and of unknown distribution. No boundary shape constraints are imposed on the object, only a weak bulk condition for the object's interior is required. The algorithm has linear complexity and exponential accuracy and is appropriate for real-time systems. In this paper, we develop further the mathematical formalism of our method and explore im- portant connections to the mathematical theory of percolation and statistical physics. We prove results on consistency and algorithmic complexity of our testing procedure. In addition, we address not only an asymptotic behavior of the method, but also a nite sample performance of our test.

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PDF [BibTex]

PDF [BibTex]


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Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models

Seeger, M., Nickisch, H.

Max Planck Institute for Biological Cybernetics, August 2010 (techreport)

Abstract
Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or super-resolution, can be addressed by maximizing the posterior distribution of a sparse linear model (SLM). We show how higher-order Bayesian decision-making problems, such as optimizing image acquisition in magnetic resonance scanners, can be addressed by querying the SLM posterior covariance, unrelated to the density's mode. We propose a scalable algorithmic framework, with which SLM posteriors over full, high-resolution images can be approximated for the first time, solving a variational optimization problem which is convex iff posterior mode finding is convex. These methods successfully drive the optimization of sampling trajectories for real-world magnetic resonance imaging through Bayesian experimental design, which has not been attempted before. Our methodology provides new insight into similarities and differences between sparse reconstruction and approximate Bayesian inference, and has important implications for compressive sensing of real-world images.

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Web [BibTex]


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Cooperative Cuts for Image Segmentation

Jegelka, S., Bilmes, J.

(UWEETR-1020-0003), University of Washington, Washington DC, USA, August 2010 (techreport)

Abstract
We propose a novel framework for graph-based cooperative regularization that uses submodular costs on graph edges. We introduce an efficient iterative algorithm to solve the resulting hard discrete optimization problem, and show that it has a guaranteed approximation factor. The edge-submodular formulation is amenable to the same extensions as standard graph cut approaches, and applicable to a range of problems. We apply this method to the image segmentation problem. Specifically, Here, we apply it to introduce a discount for homogeneous boundaries in binary image segmentation on very difficult images, precisely, long thin objects and color and grayscale images with a shading gradient. The experiments show that significant portions of previously truncated objects are now preserved.

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Web [BibTex]

Web [BibTex]


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Fast algorithms for total-variationbased optimization

Barbero, A., Sra, S.

(194), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2010 (techreport)

Abstract
We derive a number of methods to solve efficiently simple optimization problems subject to a totalvariation (TV) regularization, under different norms of the TV operator and both for the case of 1-dimensional and 2-dimensional data. In spite of the non-smooth, non-separable nature of the TV terms considered, we show that a dual formulation with strong structure can be derived. Taking advantage of this structure we develop adaptions of existing algorithms from the optimization literature, resulting in efficient methods for the problem at hand. Experimental results show that for 1-dimensional data the proposed methods achieve convergence within good accuracy levels in practically linear time, both for L1 and L2 norms. For the more challenging 2-dimensional case a performance of order O(N2 log2 N) for N x N inputs is achieved when using the L2 norm. A final section suggests possible extensions and lines of further work.

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PDF [BibTex]

PDF [BibTex]


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Gaussian Mixture Modeling with Gaussian Process Latent Variable Models

Nickisch, H., Rasmussen, C.

Max Planck Institute for Biological Cybernetics, June 2010 (techreport)

Abstract
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets.

ei

Web [BibTex]

Web [BibTex]


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Generalized Proximity and Projection with Norms and Mixed-norms

Sra, S.

(192), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, May 2010 (techreport)

Abstract
We discuss generalized proximity operators (GPO) and their associated generalized projection problems. On inputs of size n, we show how to efficiently apply GPOs and generalized projections for separable norms and distance-like functions to accuracy e in O(n log(1/e)) time. We also derive projection algorithms that run theoretically in O(n log n log(1/e)) time but can for suitable parameter ranges empirically outperform the O(n log(1/e)) projection method. The proximity and projection tasks are either separable, and solved directly, or are reduced to a single root-finding step. We highlight that as a byproduct, our analysis also yields an O(n log(1/e)) (weakly linear-time) procedure for Euclidean projections onto the l1;1-norm ball; previously only an O(n log n) method was known. We provide empirical evaluation to illustrate the performance of our methods, noting that for the l1;1-norm projection, our implementation is more than two orders of magnitude faster than the previously known method.

ei

PDF [BibTex]

PDF [BibTex]


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Cooperative Cuts: Graph Cuts with Submodular Edge Weights

Jegelka, S., Bilmes, J.

(189), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, March 2010 (techreport)

Abstract
We introduce a problem we call Cooperative cut, where the goal is to find a minimum-cost graph cut but where a submodular function is used to define the cost of a subsets of edges. That means, the cost of an edge that is added to the current cut set C depends on the edges in C. This generalization of the cost in the standard min-cut problem to a submodular cost function immediately makes the problem harder. Not only do we prove NP hardness even for nonnegative submodular costs, but also show a lower bound of Omega(|V|^(1/3)) on the approximation factor for the problem. On the positive side, we propose and compare four approximation algorithms with an overall approximation factor of min { |V|/2, |C*|, O( sqrt(|E|) log |V|), |P_max|}, where C* is the optimal solution, and P_max is the longest s, t path across the cut between given s, t. We also introduce additional heuristics for the problem which have attractive properties from the perspective of practical applications and implementations in that existing fast min-cut libraries may be used as subroutines. Both our approximation algorithms, and our heuristics, appear to do well in practice.

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PDF [BibTex]

PDF [BibTex]


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Information-theoretic inference of common ancestors

Steudel, B., Ay, N.

Computing Research Repository (CoRR), abs/1010.5720, pages: 18, 2010 (techreport)

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Web [BibTex]

Web [BibTex]


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\textscLpzRobots: A free and powerful robot simulator

Martius, G., Hesse, F., Güttler, F., Der, R.

\urlhttp://robot.informatik.uni-leipzig.de/software, 2010 (misc)

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[BibTex]

[BibTex]


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Playful Machines: Tutorial

Der, R., Martius, G.

\urlhttp://robot.informatik.uni-leipzig.de/tutorial?lang=en, 2010 (misc)

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[BibTex]

[BibTex]

2005


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Popper, Falsification and the VC-dimension

Corfield, D., Schölkopf, B., Vapnik, V.

(145), Max Planck Institute for Biological Cybernetics, November 2005 (techreport)

ei

PDF [BibTex]

2005


PDF [BibTex]


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A Combinatorial View of Graph Laplacians

Huang, J.

(144), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2005 (techreport)

Abstract
Discussions about different graph Laplacian, mainly normalized and unnormalized versions of graph Laplacian, have been ardent with respect to various methods in clustering and graph based semi-supervised learning. Previous research on graph Laplacians investigated their convergence properties to Laplacian operators on continuous manifolds. There is still no strong proof on convergence for the normalized Laplacian. In this paper, we analyze different variants of graph Laplacians directly from the ways solving the original graph partitioning problem. The graph partitioning problem is a well-known combinatorial NP hard optimization problem. The spectral solutions provide evidence that normalized Laplacian encodes more reasonable considerations for graph partitioning. We also provide some examples to show their differences.

ei

[BibTex]

[BibTex]


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Beyond Pairwise Classification and Clustering Using Hypergraphs

Zhou, D., Huang, J., Schölkopf, B.

(143), Max Planck Institute for Biological Cybernetics, August 2005 (techreport)

Abstract
In many applications, relationships among objects of interest are more complex than pairwise. Simply approximating complex relationships as pairwise ones can lead to loss of information. An alternative for these applications is to analyze complex relationships among data directly, without the need to first represent the complex relationships into pairwise ones. A natural way to describe complex relationships is to use hypergraphs. A hypergraph is a graph in which edges can connect more than two vertices. Thus we consider learning from a hypergraph, and develop a general framework which is applicable to classification and clustering for complex relational data. We have applied our framework to real-world web classification problems and obtained encouraging results.

ei

PDF [BibTex]

PDF [BibTex]


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Measuring Statistical Dependence with Hilbert-Schmidt Norms

Gretton, A., Bousquet, O., Smola, A., Schölkopf, B.

(140), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2005 (techreport)

Abstract
We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

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PDF [BibTex]

PDF [BibTex]


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Adhesive microstructure and method of forming same

Fearing, R. S., Sitti, M.

March 2005, US Patent 6,872,439 (misc)

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[BibTex]

[BibTex]


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Approximate Inference for Robust Gaussian Process Regression

Kuss, M., Pfingsten, T., Csato, L., Rasmussen, C.

(136), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005 (techreport)

Abstract
Gaussian process (GP) priors have been successfully used in non-parametric Bayesian regression and classification models. Inference can be performed analytically only for the regression model with Gaussian noise. For all other likelihood models inference is intractable and various approximation techniques have been proposed. In recent years expectation-propagation (EP) has been developed as a general method for approximate inference. This article provides a general summary of how expectation-propagation can be used for approximate inference in Gaussian process models. Furthermore we present a case study describing its implementation for a new robust variant of Gaussian process regression. To gain further insights into the quality of the EP approximation we present experiments in which we compare to results obtained by Markov chain Monte Carlo (MCMC) sampling.

ei

PDF [BibTex]

PDF [BibTex]


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Maximum-Margin Feature Combination for Detection and Categorization

BakIr, G., Wu, M., Eichhorn, J.

Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005 (techreport)

Abstract
In this paper we are concerned with the optimal combination of features of possibly different types for detection and estimation tasks in machine vision. We propose to combine features such that the resulting classifier maximizes the margin between classes. In contrast to existing approaches which are non-convex and/or generative we propose to use a discriminative model leading to convex problem formulation and complexity control. Furthermore we assert that decision functions should not compare apples and oranges by comparing features of different types directly. Instead we propose to combine different similarity measures for each different feature type. Furthermore we argue that the question: ”Which feature type is more discriminative for task X?” is ill-posed and show empirically that the answer to this question might depend on the complexity of the decision function.

ei

PDF [BibTex]

PDF [BibTex]


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Towards a Statistical Theory of Clustering. Presented at the PASCAL workshop on clustering, London

von Luxburg, U., Ben-David, S.

Presented at the PASCAL workshop on clustering, London, 2005 (techreport)

Abstract
The goal of this paper is to discuss statistical aspects of clustering in a framework where the data to be clustered has been sampled from some unknown probability distribution. Firstly, the clustering of the data set should reveal some structure of the underlying data rather than model artifacts due to the random sampling process. Secondly, the more sample points we have, the more reliable the clustering should be. We discuss which methods can and cannot be used to tackle those problems. In particular we argue that generalization bounds as they are used in statistical learning theory of classification are unsuitable in a general clustering framework. We suggest that the main replacements of generalization bounds should be convergence proofs and stability considerations. This paper should be considered as a road map paper which identifies important questions and potentially fruitful directions for future research about statistical clustering. We do not attempt to present a complete statistical theory of clustering.

ei

PDF [BibTex]

PDF [BibTex]


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Approximate Bayesian Inference for Psychometric Functions using MCMC Sampling

Kuss, M., Jäkel, F., Wichmann, F.

(135), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2005 (techreport)

Abstract
In psychophysical studies the psychometric function is used to model the relation between the physical stimulus intensity and the observer's ability to detect or discriminate between stimuli of different intensities. In this report we propose the use of Bayesian inference to extract the information contained in experimental data estimate the parameters of psychometric functions. Since Bayesian inference cannot be performed analytically we describe how a Markov chain Monte Carlo method can be used to generate samples from the posterior distribution over parameters. These samples are used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. In addition we discuss the parameterisation of psychometric functions and the role of prior distributions in the analysis. The proposed approach is exemplified using artificially generate d data and in a case study for real experimental data. Furthermore, we compare our approach with traditional methods based on maximum-likelihood parameter estimation combined with bootstrap techniques for confidence interval estimation. The appendix provides a description of an implementation for the R environment for statistical computing and provides the code for reproducing the results discussed in the experiment section.

ei

PDF [BibTex]

PDF [BibTex]


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Linear and Nonlinear Estimation models applied to Hemodynamic Model

Theodorou, E.

Technical Report-2005-1, Computational Action and Vision Lab University of Minnesota, 2005, clmc (techreport)

Abstract
The relation between BOLD signal and neural activity is still poorly understood. The Gaussian Linear Model known as GLM is broadly used in many fMRI data analysis for recovering the underlying neural activity. Although GLM has been proved to be a really useful tool for analyzing fMRI data it can not be used for describing the complex biophysical process of neural metabolism. In this technical report we make use of a system of Stochastic Differential Equations that is based on Buxton model [1] for describing the underlying computational principles of hemodynamic process. Based on this SDE we built a Kalman Filter estimator so as to estimate the induced neural signal as well as the blood inflow under physiologic and sensor noise. The performance of Kalman Filter estimator is investigated under different physiologic noise characteristics and measurement frequencies.

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PDF [BibTex]

PDF [BibTex]