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Change-Point Detection using Krylov Subspace Learning

2007

Conference Paper

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We propose an efficient algorithm for principal component analysis (PCA) that is applicable when only the inner product with a given vector is needed. We show that Krylov subspace learning works well both in matrix compression and implicit calculation of the inner product by taking full advantage of the arbitrariness of the seed vector. We apply our algorithm to a PCA-based change-point detection algorithm, and show that it results in about 50 times improvement in computational time.

Author(s): Ide, T. and Tsuda, K.
Book Title: SDM 2007
Journal: Proceedings of the SIAM International Conference on Data Mining (SDM 2007)
Pages: 515-520
Year: 2007
Month: April
Day: 0
Editors: Apte, C.
Publisher: Society for Industrial and Applied Mathematics

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: SIAM International Conference on Data Mining
Event Place: Minneapolis, MN, USA

Address: Pittsburgh, PA, USA
Digital: 0
Institution: Society for Industrial and Applied Mathematics
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{4399,
  title = {Change-Point Detection using Krylov Subspace Learning},
  author = {Ide, T. and Tsuda, K.},
  journal = {Proceedings of the SIAM International Conference on Data Mining (SDM 2007)},
  booktitle = {SDM 2007},
  pages = {515-520},
  editors = {Apte, C. },
  publisher = {Society for Industrial and Applied Mathematics},
  organization = {Max-Planck-Gesellschaft},
  institution = {Society for Industrial and Applied Mathematics},
  school = {Biologische Kybernetik},
  address = {Pittsburgh, PA, USA},
  month = apr,
  year = {2007},
  month_numeric = {4}
}