Clustering with the fisher score

Koji Tsuda, Motoaki Kawanabe, Klaus Robert Müller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

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).

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002
PublisherNeural information processing systems foundation
ISBN (Print)0262025507, 9780262025508
Publication statusPublished - 2003
Externally publishedYes
Event16th Annual Neural Information Processing Systems Conference, NIPS 2002 - Vancouver, BC, Canada
Duration: 2002 Dec 92002 Dec 14

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other16th Annual Neural Information Processing Systems Conference, NIPS 2002
Country/TerritoryCanada
CityVancouver, BC
Period02/12/902/12/14

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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