Text retrieval using sparsified concept decomposition matrix

Jing Gao; Jun Zhang

doi:10.1007/978-3-540-30497-5_82

Text retrieval using sparsified concept decomposition matrix

Jing Gao, Jun Zhang

Computer Science

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

We examine text retrieval strategies using the sparsified concept decomposition matrix. The centroid vector of a tightly structured text collection provides a general description of text documents in that collection. The union of the centroid vectors forms a concept matrix. The original text data matrix can be projected into the concept space spanned by the concept vectors. We propose a procedure to conduct text retrieval based on the sparsified concept decomposition (SCD) matrix. Our experimental results show that text retrieval based on SCD may enhance the retrieval accuracy and reduce the storage cost, compared with the popular text retrieval technique based on latent semantic indexing with singular value decomposition.

Original language	English
Pages (from-to)	523-529
Number of pages	7
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3314
DOIs	https://doi.org/10.1007/978-3-540-30497-5_82
State	Published - 2004

Bibliographical note

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

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-540-30497-5_82

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title = "Text retrieval using sparsified concept decomposition matrix",

abstract = "We examine text retrieval strategies using the sparsified concept decomposition matrix. The centroid vector of a tightly structured text collection provides a general description of text documents in that collection. The union of the centroid vectors forms a concept matrix. The original text data matrix can be projected into the concept space spanned by the concept vectors. We propose a procedure to conduct text retrieval based on the sparsified concept decomposition (SCD) matrix. Our experimental results show that text retrieval based on SCD may enhance the retrieval accuracy and reduce the storage cost, compared with the popular text retrieval technique based on latent semantic indexing with singular value decomposition.",

author = "Jing Gao and Jun Zhang",

note = "Funding Information: ★ The research work of the authors was supported in part by the U.S. National Science Foundation under grants CCR-0092532, and ACR-0202934, and in part by the U.S. Department of Energy Office of Science under grant DE-FG02-02ER45961.",

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AU - Gao, Jing

AU - Zhang, Jun

N1 - Funding Information: ★ The research work of the authors was supported in part by the U.S. National Science Foundation under grants CCR-0092532, and ACR-0202934, and in part by the U.S. Department of Energy Office of Science under grant DE-FG02-02ER45961.

PY - 2004

Y1 - 2004

N2 - We examine text retrieval strategies using the sparsified concept decomposition matrix. The centroid vector of a tightly structured text collection provides a general description of text documents in that collection. The union of the centroid vectors forms a concept matrix. The original text data matrix can be projected into the concept space spanned by the concept vectors. We propose a procedure to conduct text retrieval based on the sparsified concept decomposition (SCD) matrix. Our experimental results show that text retrieval based on SCD may enhance the retrieval accuracy and reduce the storage cost, compared with the popular text retrieval technique based on latent semantic indexing with singular value decomposition.

AB - We examine text retrieval strategies using the sparsified concept decomposition matrix. The centroid vector of a tightly structured text collection provides a general description of text documents in that collection. The union of the centroid vectors forms a concept matrix. The original text data matrix can be projected into the concept space spanned by the concept vectors. We propose a procedure to conduct text retrieval based on the sparsified concept decomposition (SCD) matrix. Our experimental results show that text retrieval based on SCD may enhance the retrieval accuracy and reduce the storage cost, compared with the popular text retrieval technique based on latent semantic indexing with singular value decomposition.

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

Text retrieval using sparsified concept decomposition matrix

Abstract

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ASJC Scopus subject areas

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