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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Nyffenegger, M., Chappelier, J. C., & Gaussier, É. (2006). Revisiting fisher kernels for document similarities. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4212 LNAI, pp. 727–734). Springer Verlag. https://doi.org/10.1007/11871842_73
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