Header logo is

Fast Projection-based Methods for the Least Squares Nonnegative Matrix Approximation Problem




Nonnegative matrix approximation (NNMA) is a popular matrix decomposition technique that has proven to be useful across a diverse variety of fields with applications ranging from document analysis and image processing to bioinformatics and signal processing. Over the years, several algorithms for NNMA have been proposed, e.g. Lee and Seung‘s multiplicative updates, alternating least squares (ALS), and gradient descent-based procedures. However, most of these procedures suffer from either slow convergence, numerical instability, or at worst, serious theoretical drawbacks. In this paper, we develop a new and improved algorithmic framework for the least-squares NNMA problem, which is not only theoretically well-founded, but also overcomes many deficiencies of other methods. Our framework readily admits powerful optimization techniques and as concrete realizations we present implementations based on the Newton, BFGS and conjugate gradient methods. Our algorithms provide numerical resu lts supe rior to both Lee and Seung‘s method as well as to the alternating least squares heuristic, which was reported to work well in some situations but has no theoretical guarantees[1]. Our approach extends naturally to include regularization and box-constraints without sacrificing convergence guarantees. We present experimental results on both synthetic and real-world datasets that demonstrate the superiority of our methods, both in terms of better approximations as well as computational efficiency.

Author(s): Kim, D. and Sra, S. and Dhillon, I.
Journal: Statistical Analysis and Data Mining
Volume: 1
Number (issue): 1
Pages: 38-51
Year: 2008
Month: February
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1002/sam.104
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Fast Projection-based Methods for the Least Squares Nonnegative Matrix Approximation Problem},
  author = {Kim, D. and Sra, S. and Dhillon, I.},
  journal = {Statistical Analysis and Data Mining},
  volume = {1},
  number = {1},
  pages = {38-51},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = feb,
  year = {2008},
  month_numeric = {2}