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Clustering with the Fisher score

2003

Conference Paper

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Recently the Fisher score (or the Fisher kernel) is increasingly used as a feature extractor for classification problems. The Fisher score is a vector of parameter derivatives of loglikelihood of a probabilistic model. This paper gives a theoretical analysis about how class information is preserved in the space of the Fisher score, which turns out that the Fisher score consists of a few important dimensions with class information and many nuisance dimensions. When we perform clustering with the Fisher score, K-Means type methods are obviously inappropriate because they make use of all dimensions. So we will develop a novel but simple clustering algorithm specialized for the Fisher score, which can exploit important dimensions. This algorithm is successfully tested in experiments with artificial data and real data (amino acid sequences).

Author(s): Tsuda, K. and Kawanabe, M. and Müller, KR.
Book Title: Advances in Neural Information Processing Systems 15
Journal: Advances in Neural Information Processing Systems 15
Pages: 729-736
Year: 2003
Month: October
Day: 0
Editors: Becker, S. , S. Thrun, K. Obermayer
Publisher: MIT Press

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

Event Name: Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-02550-7
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{2175,
  title = {Clustering with the Fisher score},
  author = {Tsuda, K. and Kawanabe, M. and M{\"u}ller, KR.},
  journal = {Advances in Neural Information Processing Systems 15},
  booktitle = {Advances in Neural Information Processing Systems 15},
  pages = {729-736},
  editors = {Becker, S. , S. Thrun, K. Obermayer},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = oct,
  year = {2003},
  month_numeric = {10}
}