Multidimensional latent semantic analysis using term spatial information

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

24 Scopus Citations
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Author(s)

  • Haijun Zhang
  • John K.L. Ho
  • Q.M. Jonathan Wu
  • Yunming Ye

Detail(s)

Original languageEnglish
Article number6670128
Pages (from-to)1625-1640
Journal / PublicationIEEE Transactions on Cybernetics
Volume43
Issue number6
Online published21 Jan 2013
Publication statusPublished - Dec 2013

Abstract

In this paper, we consider the problem of in-depth document analysis. In particular, we propose a novel document analysis method, named multidimensional latent semantic analysis (MDLSA), which enables us to mine local information efficiently from a document with respect to term associations and spatial distributions. MDLSA works by first partitioning each document into paragraphs and building a term affinity graph, which represents the frequency of term cooccurrence in a paragraph. We then conduct a 2-D principal component analysis to achieve an optimal semantic mapping. This analysis involves finding the leading eigenvectors of the sample covariance matrix of a training set to characterize the lower dimensional semantic space. A hybrid document similarity measure is designed to further improve the performance of this framework. Our algorithm is examined in two document applications: retrieval and classification. Experimental results demonstrate that the proposed technique outperforms current algorithms with respect to accuracy and computational efficiency. © 2013 IEEE.

Research Area(s)

  • Dimensionality reduction, Multidimensional, Principle component analysis (PCA), Semantic analysis, Term association

Citation Format(s)

Multidimensional latent semantic analysis using term spatial information. / Zhang, Haijun; Ho, John K.L.; Wu, Q.M. Jonathan et al.
In: IEEE Transactions on Cybernetics, Vol. 43, No. 6, 6670128, 12.2013, p. 1625-1640.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review