Latent semantic indexing using eigenvalue analysis for efficient information retrieval

• Autorzy: Aswani Kumar Ch. , Srinivas S.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

information retrieval; latent semantic indexing; eigenvalues; rank reduction; singular value decomdecomposition; vector space method

PL

wyszukiwanie informacji; indeksowanie semantyczne; wartość własna; wektor przestrzenny

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2006
• Tom: Vol. 16, no 4
• Strony: 551--558
• Opis fizyczny: Bibliogr. 29 poz., tab., wykr.

Twórcy

autor

Aswani Kumar Ch.

• School of Computing Sciences, Vellore Institute of Technology, Deemed University, Vellore – 632014, India

autor

Srinivas S.

• School of Science and Humanities, Vellore Institute of Technology, Deemed University, Vellore – 632014, India

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