Machine Learning: ECML 2006

Volume 4212 of the series Lecture Notes in Computer Science pp 727-734

Revisiting Fisher Kernels for Document Similarities

  • Martin NyffeneggerAffiliated withEcole Polytechnique Fédérale de Lausanne
  • , Jean-Cédric ChappelierAffiliated withEcole Polytechnique Fédérale de Lausanne
  • , Éric GaussierAffiliated withXerox Research Center Europe

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This paper presents a new metric to compute similarities between textual documents, based on the Fisher information kernel as proposed by T. Hofmann. By considering a new point-of-view on the embedding vector space and proposing a more appropriate way of handling the Fisher information matrix, we derive a new form of the kernel that yields significant improvements on an information retrieval task. We apply our approach to two different models: Naive Bayes and PLSI.