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NeuroCOLT
Technical Report NC-TR-00-080
Latent Semantic Kernels for Feature Selection
Nello Cristianini, John Shawe-Taylor, Huma Lodhi
Department
of Computer Science
Royal Holloway, University of London
Abstract
Latent Semantic Indexing is a method for selecting informative subspaces
of feature spaces. It was developed for information retrieval to reveal
semantic information from document co-occurrences. The paper demonstrates
how this method can be implemented implicitly to a kernel defined
feature space and hence adapted for application to any kernel based
learning algorithm and data. Experiments with text and UCI data show
the technique can improve generalisation performance by focussing
attention of a Support Vector Machine onto informative subspaces of
the feature space.
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