Multidimensional latent semantic analysis using term spatial information
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Article number | 6670128 |
Pages (from-to) | 1625-1640 |
Journal / Publication | IEEE Transactions on Cybernetics |
Volume | 43 |
Issue number | 6 |
Online published | 21 Jan 2013 |
Publication status | Published - Dec 2013 |
Link(s)
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.
In: IEEE Transactions on Cybernetics, Vol. 43, No. 6, 6670128, 12.2013, p. 1625-1640.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review