Abstract
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.
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Nyffenegger, M., Chappelier, JC., Gaussier, É. (2006). Revisiting Fisher Kernels for Document Similarities. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds) Machine Learning: ECML 2006. ECML 2006. Lecture Notes in Computer Science(), vol 4212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11871842_73
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DOI: https://doi.org/10.1007/11871842_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45375-8
Online ISBN: 978-3-540-46056-5
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