Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A probabilistic model of information retrieval: development and comparative experiments Part 2
Information Processing and Management: an International Journal
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Hybrid Pre-Query Term Expansion using Latent Semantic Analysis
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A probabilistic model for Latent Semantic Indexing: Research Articles
Journal of the American Society for Information Science and Technology
Eigenvalue-based model selection during latent semantic indexing: Research Articles
Journal of the American Society for Information Science and Technology
A framework for understanding latent semantic indexing (LSI) performance
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
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An analysis of latent semantic term self-correlation
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The VLDB Journal — The International Journal on Very Large Data Bases
Query expansion using a collection dependent probabilistic latent semantic thesaurus
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Trading spaces: on the lore and limitations of latent semantic analysis
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
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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.