Article

Locality preserving indexing for document representation

Authors:

Wei-Ying MaAuthors Info & Claims

SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 96 - 103

https://doi.org/10.1145/1008992.1009012

Published: 25 July 2004 Publication History

Abstract

Document representation and indexing is a key problem for document analysis and processing, such as clustering, classification and retrieval. Conventionally, Latent Semantic Indexing (LSI) is considered effective in deriving such an indexing. LSI essentially detects the most representative features for document representation rather than the most discriminative features. Therefore, LSI might not be optimal in discriminating documents with different semantics. In this paper, a novel algorithm called Locality Preserving Indexing (LPI) is proposed for document indexing. Each document is represented by a vector with low dimensionality. In contrast to LSI which discovers the global structure of the document space, LPI discovers the local structure and obtains a compact document representation subspace that best detects the essential semantic structure. We compare the proposed LPI approach with LSI on two standard databases. Experimental results show that LPI provides better representation in the sense of semantic structure.

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Cited By

Taştan AMuma MZoubir A(2024)Robust Regularized Locality Preserving Indexing for Fiedler Vector EstimationIEEE Open Journal of Signal Processing10.1109/OJSP.2024.34006835(867-885)Online publication date: 2024
https://doi.org/10.1109/OJSP.2024.3400683
Li XTyagi A(2023)Block-Active ADMM to Minimize NMF with Bregman DivergencesSensors10.3390/s2316722923:16(7229)Online publication date: 17-Aug-2023
https://doi.org/10.3390/s23167229
Li SHuang YKasukurthi MYang JLi DYang GLin JTan SBourrie DMa BBorchert GHuang J(2021)A New Classification Algorithm and a New Oversampling Method of Mapping Common Data Elements to the BRIDG Model2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM52615.2021.9669697(2788-2795)Online publication date: 9-Dec-2021
https://doi.org/10.1109/BIBM52615.2021.9669697
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Index Terms

Locality preserving indexing for document representation
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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Published In

cover image ACM Conferences

SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

July 2004

624 pages

ISBN:1581138814

DOI:10.1145/1008992

General Chair:
Mark Sanderson
University of Sheffield (UK)
,
Program Chairs:
Kalervo Järvelin
University of Tampere (Finland)
,
James Allan
University of Massachusetts (USA)
,
Peter Bruza
Distributed Systems Technology Centre (Australia)

Copyright © 2004 ACM.

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Published: 25 July 2004

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SIGIR04: The 27th ACM/SIGIR International Symposium on Information Retrieval 2004

July 25 - 29, 2004

Sheffield, United Kingdom

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Taştan AMuma MZoubir A(2024)Robust Regularized Locality Preserving Indexing for Fiedler Vector EstimationIEEE Open Journal of Signal Processing10.1109/OJSP.2024.34006835(867-885)Online publication date: 2024
https://doi.org/10.1109/OJSP.2024.3400683
Li XTyagi A(2023)Block-Active ADMM to Minimize NMF with Bregman DivergencesSensors10.3390/s2316722923:16(7229)Online publication date: 17-Aug-2023
https://doi.org/10.3390/s23167229
Li SHuang YKasukurthi MYang JLi DYang GLin JTan SBourrie DMa BBorchert GHuang J(2021)A New Classification Algorithm and a New Oversampling Method of Mapping Common Data Elements to the BRIDG Model2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM52615.2021.9669697(2788-2795)Online publication date: 9-Dec-2021
https://doi.org/10.1109/BIBM52615.2021.9669697
Chen LLiu XLi Z(2021)Nonlinear Graph Learning-Convolutional Networks for Node ClassificationNeural Processing Letters10.1007/s11063-021-10478-x54:4(2727-2736)Online publication date: 10-Mar-2021
https://doi.org/10.1007/s11063-021-10478-x
Khalpada VKoringa PMitra S(2019)Document Representation using Extended Locality Preserving Indexing2019 IEEE 16th India Council International Conference (INDICON)10.1109/INDICON47234.2019.9030348(1-4)Online publication date: Dec-2019
https://doi.org/10.1109/INDICON47234.2019.9030348
Hanif ODonghua ZXuefeng WNawaz M(2019)Refining the Measurement of Topic Similarities Through Bibliographic Coupling and LDAIEEE Access10.1109/ACCESS.2019.29584897(179997-180011)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2958489
Relan DBallerini LTrucco EMacgillivray T(2019)Using orthogonal locality preserving projections to find dominant features for classifying retinal blood vesselsMultimedia Tools and Applications10.1007/s11042-018-6474-778:10(12783-12803)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s11042-018-6474-7
Liu HZhang ZZhang XYang YZhang ZLiu XWang FHan YZhang C(2018)Dimensionality Reduction for Identification of Hepatic Tumor Samples Based on Terahertz Time-Domain SpectroscopyIEEE Transactions on Terahertz Science and Technology10.1109/TTHZ.2018.28130858:3(271-277)Online publication date: May-2018
https://doi.org/10.1109/TTHZ.2018.2813085
Maheshan MHarish BRevanasiddappa M(2018)Indexing-Based Classification: An Approach Toward Classifying Text DocumentsInformation Systems Design and Intelligent Applications10.1007/978-981-10-7512-4_88(894-902)Online publication date: 2-Mar-2018
https://doi.org/10.1007/978-981-10-7512-4_88
Ma XWang J(2017)Image Retrieval Using Deep Convolutional Neural Networks and Regularized Locality Preserving Indexing StrategyJournal of Computer and Communications10.4236/jcc.2017.5300405:03(33-39)Online publication date: 2017
https://doi.org/10.4236/jcc.2017.53004
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