Online and Batch Learning of Generalized Cosine Similarities

Ali Mustafa Qamar 1 Eric Gaussier 2
2 MRIM - Modélisation et Recherche d’Information Multimédia [Grenoble]
LIG - Laboratoire d'Informatique de Grenoble, Inria - Institut National de Recherche en Informatique et en Automatique
Abstract : In this paper, we define an online algorithm to learn the generalized cosine similarity measures for kNN classification and hence a similarity matrix A corresponding to a bilinear form. In contrary to the standard cosine measure, the normalization is itself dependent on the similarity matrix which makes it impossible to use directly the algorithms developed for learning Mahanalobis distances, based on positive, semi-definite (PSD) matrices. We follow the approach where we first find an appropriate matrix and then project it onto the cone of PSD matrices, which we have adapted to the particular form of generalized cosine similarities, and more particularly to the fact that such measures are normalized. The resulting online algorithm as well as its batch version is fast and has got better accuracy as compared with state-of-the-art methods on standard data sets.
Type de document :
Communication dans un congrès
IEEE International Conference on Data Mining (ICDM), 2009, Florida, United States. pp.926-931, 2009
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https://hal.inria.fr/hal-00953853
Contributeur : Marie-Christine Fauvet <>
Soumis le : vendredi 28 février 2014 - 16:02:27
Dernière modification le : mardi 24 avril 2018 - 13:29:21

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  • HAL Id : hal-00953853, version 1

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Ali Mustafa Qamar, Eric Gaussier. Online and Batch Learning of Generalized Cosine Similarities. IEEE International Conference on Data Mining (ICDM), 2009, Florida, United States. pp.926-931, 2009. 〈hal-00953853〉

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