A new discriminative Kernel from probabilistic models

Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus Robert Müller

Research output: Contribution to journalArticle

79 Citations (Scopus)

Abstract

Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.

Original languageEnglish
Pages (from-to)2397-2414
Number of pages18
JournalNeural Computation
Volume14
Issue number10
DOIs
Publication statusPublished - 2002 Oct

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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    Tsuda, K., Kawanabe, M., Rätsch, G., Sonnenburg, S., & Müller, K. R. (2002). A new discriminative Kernel from probabilistic models. Neural Computation, 14(10), 2397-2414. https://doi.org/10.1162/08997660260293274