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2005
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Text classification with kernels on the multinomial manifold

10 years 7 months ago
Text classification with kernels on the multinomial manifold
Support Vector Machines (SVMs) have been very successful in text classification. However, the intrinsic geometric structure of text data has been ignored by standard kernels commonly used in SVMs. It is natural to assume that the documents are on the multinomial manifold, which is the simplex of multinomial models furnished with the Riemannian structure induced by the Fisher information metric. We prove that the Negative Geodesic Distance (NGD) on the multinomial manifold is conditionally positive definite (cpd), thus can be used as a kernel in SVMs. Experiments show the NGD kernel on the multinomial manifold to be effective for text classification, significantly outperforming standard kernels on the ambient Euclidean space. Categories and Subject Descriptors
Dell Zhang, Xi Chen, Wee Sun Lee
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where SIGIR
Authors Dell Zhang, Xi Chen, Wee Sun Lee
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