TDM modeling and evaluation of different domain transforms for LSI

T Jaber; A Amira; P Milligan

doi:10.1016/j.neucom.2008.12.010

TDM modeling and evaluation of different domain transforms for LSI

T Jaber, A Amira, P Milligan

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

2 Citations (Scopus)

Abstract

Latent semantic indexing (LSI) is a popular technique used in information retrieval (IR) applications. This paper presents a novel evaluation strategy based on the use of image processing tools. The authors evaluate the use of the discrete cosine transform (DCT) and Cohen Daubechies Feauveau 9/7 (CDF9/7) wavelet transform as a preprocessing step for the singular value decomposition (SVD) step of the LSI system. In addition, the effect of different threshold types on the search results is examined. The results show that accuracy can be increased by applying both transforms as a preprocessing step, with better performance for the hard-threshold function. The choice of the best threshold value is a key factor in the transform process. This paper also describes the most effective structure for the database to facilitate efficient searching in the LSI system. (c) 2009 Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	2406-2417
Journal	Neurocomputing
Volume	72
Issue number	10-12,
DOIs	https://doi.org/10.1016/j.neucom.2008.12.010
Publication status	Published (in print/issue) - Jun 2009

Keywords

Latent semantic indexing
Information retrieval
Discrete cosine transform
Singular value decomposition
Cohen Daubechies Feauveau 9/7
Hard thresholding
Soft thresholding

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10.1016/j.neucom.2008.12.010

Cite this

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title = "TDM modeling and evaluation of different domain transforms for LSI",

abstract = "Latent semantic indexing (LSI) is a popular technique used in information retrieval (IR) applications. This paper presents a novel evaluation strategy based on the use of image processing tools. The authors evaluate the use of the discrete cosine transform (DCT) and Cohen Daubechies Feauveau 9/7 (CDF9/7) wavelet transform as a preprocessing step for the singular value decomposition (SVD) step of the LSI system. In addition, the effect of different threshold types on the search results is examined. The results show that accuracy can be increased by applying both transforms as a preprocessing step, with better performance for the hard-threshold function. The choice of the best threshold value is a key factor in the transform process. This paper also describes the most effective structure for the database to facilitate efficient searching in the LSI system. (c) 2009 Elsevier B.V. All rights reserved.",

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doi = "10.1016/j.neucom.2008.12.010",

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T1 - TDM modeling and evaluation of different domain transforms for LSI

AU - Jaber, T

AU - Amira, A

AU - Milligan, P

PY - 2009/6

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N2 - Latent semantic indexing (LSI) is a popular technique used in information retrieval (IR) applications. This paper presents a novel evaluation strategy based on the use of image processing tools. The authors evaluate the use of the discrete cosine transform (DCT) and Cohen Daubechies Feauveau 9/7 (CDF9/7) wavelet transform as a preprocessing step for the singular value decomposition (SVD) step of the LSI system. In addition, the effect of different threshold types on the search results is examined. The results show that accuracy can be increased by applying both transforms as a preprocessing step, with better performance for the hard-threshold function. The choice of the best threshold value is a key factor in the transform process. This paper also describes the most effective structure for the database to facilitate efficient searching in the LSI system. (c) 2009 Elsevier B.V. All rights reserved.

AB - Latent semantic indexing (LSI) is a popular technique used in information retrieval (IR) applications. This paper presents a novel evaluation strategy based on the use of image processing tools. The authors evaluate the use of the discrete cosine transform (DCT) and Cohen Daubechies Feauveau 9/7 (CDF9/7) wavelet transform as a preprocessing step for the singular value decomposition (SVD) step of the LSI system. In addition, the effect of different threshold types on the search results is examined. The results show that accuracy can be increased by applying both transforms as a preprocessing step, with better performance for the hard-threshold function. The choice of the best threshold value is a key factor in the transform process. This paper also describes the most effective structure for the database to facilitate efficient searching in the LSI system. (c) 2009 Elsevier B.V. All rights reserved.

KW - Latent semantic indexing

KW - Information retrieval

KW - Discrete cosine transform

KW - Singular value decomposition

KW - Cohen Daubechies Feauveau 9/7

KW - Hard thresholding

KW - Soft thresholding

U2 - 10.1016/j.neucom.2008.12.010

DO - 10.1016/j.neucom.2008.12.010

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SP - 2406

EP - 2417

JO - Neurocomputing

JF - Neurocomputing

IS - 10-12,

ER -

TDM modeling and evaluation of different domain transforms for LSI

Abstract

Keywords

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