# Latent Semantic Indexing using eigenvalue analysis for efficient information retrieval

Cherukuri Kumar; Suripeddi Srinivas

International Journal of Applied Mathematics and Computer Science (2006)

- Volume: 16, Issue: 4, page 551-558
- ISSN: 1641-876X

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topKumar, Cherukuri, and Srinivas, Suripeddi. "Latent Semantic Indexing using eigenvalue analysis for efficient information retrieval." International Journal of Applied Mathematics and Computer Science 16.4 (2006): 551-558. <http://eudml.org/doc/207813>.

@article{Kumar2006,

abstract = {Text retrieval using Latent Semantic Indexing (LSI) with truncated Singular Value Decomposition (SVD) has been intensively studied in recent years. However, the expensive complexity involved in computing truncated SVD constitutes a major drawback of the LSI method. In this paper, we demonstrate how matrix rank approximation can influence the effectiveness of information retrieval systems. Besides, we present an implementation of the LSI method based on an eigenvalue analysis for rank approximation without computing truncated SVD, along with its computational details. Significant improvements in computational time while maintaining retrieval accuracy are observed over the tested document collections.},

author = {Kumar, Cherukuri, Srinivas, Suripeddi},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {eigenvalues; latent semantic indexing; vector space method; rank reduction; information retrieval; singular value decomposition},

language = {eng},

number = {4},

pages = {551-558},

title = {Latent Semantic Indexing using eigenvalue analysis for efficient information retrieval},

url = {http://eudml.org/doc/207813},

volume = {16},

year = {2006},

}

TY - JOUR

AU - Kumar, Cherukuri

AU - Srinivas, Suripeddi

TI - Latent Semantic Indexing using eigenvalue analysis for efficient information retrieval

JO - International Journal of Applied Mathematics and Computer Science

PY - 2006

VL - 16

IS - 4

SP - 551

EP - 558

AB - Text retrieval using Latent Semantic Indexing (LSI) with truncated Singular Value Decomposition (SVD) has been intensively studied in recent years. However, the expensive complexity involved in computing truncated SVD constitutes a major drawback of the LSI method. In this paper, we demonstrate how matrix rank approximation can influence the effectiveness of information retrieval systems. Besides, we present an implementation of the LSI method based on an eigenvalue analysis for rank approximation without computing truncated SVD, along with its computational details. Significant improvements in computational time while maintaining retrieval accuracy are observed over the tested document collections.

LA - eng

KW - eigenvalues; latent semantic indexing; vector space method; rank reduction; information retrieval; singular value decomposition

UR - http://eudml.org/doc/207813

ER -

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