Journal of Intelligent Information Systems

, Volume 18, Issue 2–3, pp 127–152

Latent Semantic Kernels

  • Nello Cristianini
  • John Shawe-Taylor
  • Huma Lodhi
Article

Abstract

Kernel methods like support vector machines have successfully been used for text categorization. A standard choice of kernel function has been the inner product between the vector-space representation of two documents, in analogy with classical information retrieval (IR) approaches.

Latent semantic indexing (LSI) has been successfully used for IR purposes as a technique for capturing semantic relations between terms and inserting them into the similarity measure between two documents. One of its main drawbacks, in IR, is its computational cost.

In this paper we describe how the LSI approach can be implemented in a kernel-defined feature space.

We provide experimental results demonstrating that the approach can significantly improve performance, and that it does not impair it.

Kernel methods latent semantic indexing latent semantic kernels Gram-Schmidt kernels text categorization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Nello Cristianini
    • 1
  • John Shawe-Taylor
    • 1
  • Huma Lodhi
    • 1
  1. 1.Department of Computer Science, Royal HollowayUniversity of LondonEgham, SurreyUK

Personalised recommendations