Kernel latent semantic analysis using an information retrieval based kernel

Hidden term relationships can be found within a document collection using Latent semantic analysis (LSA) and can be used to assist in information retrieval. LSA uses the inner product as its similarity function, which unfortunately introduces bias due to document length and term rarity into the term relationships. In this article, we present the novel kernel based LSA method, which uses separate document and query kernel functions to compute document and query similarities, rather than the inner product. We show that by providing an appropriate kernel function, we are able to provide a better fit of our data and hence produce more effective term relationships.