Latent semantic kernels for feature selection

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 de ned 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.