Clustered SVD strategies in latent semantic indexing

Jing Gao, Jun Zhang

Research output: Contribution to journalArticlepeer-review

53 Scopus citations

Abstract

The text retrieval method using latent semantic indexing (LSI) technique with truncated singular value decomposition (SVD) has been intensively studied in recent years. The SVD reduces the noise contained in the original representation of the term-document matrix and improves the information retrieval accuracy. Recent studies indicate that SVD is mostly useful for small homogeneous data collections. For large inhomogeneous datasets, the performance of the SVD based text retrieval technique may deteriorate. We propose to partition a large inhomogeneous dataset into several smaller ones with clustered structure, on which we apply the truncated SVD. Our experimental results show that the clustered SVD strategies may enhance the retrieval accuracy and reduce the computing and storage costs.

Original languageEnglish
Pages (from-to)1051-1063
Number of pages13
JournalInformation Processing and Management
Volume41
Issue number5
DOIs
StatePublished - Sep 2005

Bibliographical note

Funding Information:
The research work of the authors was supported in part by the US National Science Foundation under grants CCR-9988165, CCR-0092532, ACR-0202934, and ACR-0234270, and by the US Department of Energy Office of Science under grant DE-FG02-02ER45961.

Keywords

  • Clustering
  • Latent semantic indexing
  • SVD
  • Text retrieval

ASJC Scopus subject areas

  • Information Systems
  • Media Technology
  • Computer Science Applications
  • Management Science and Operations Research
  • Library and Information Sciences

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