Comparison of Matrix Dimensionality Reduction Methods in Uncovering Latent Structures in the Data

Ch. Aswani Kumar () and Ramaraj Palanisamy ()
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Ch. Aswani Kumar: Networks and Information Security Division, School of Information Technology and Engineering, VIT University, Vellore 632014, India
Ramaraj Palanisamy: Department of Information Systems, St. Francis Xavier University, Canada

Journal of Information & Knowledge Management (JIKM), 2010, vol. 09, issue 01, 81-92

Abstract: Matrix decomposition methods: Singular Value Decomposition (SVD) and Semi Discrete Decomposition (SDD) are proved to be successful in dimensionality reduction. However, to the best of our knowledge, no empirical results are presented and no comparison between these methods is done to uncover latent structures in the data. In this paper, we present how these methods can be used to identify and visualise latent structures in the time series data. Results on a high dimensional dataset demonstrate that SVD is more successful in uncovering the latent structures.

Keywords: Data mining; dimensionality reduction; principal component analysis; Semi Discrete Decomposition; Singular Value Decomposition (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1142/S0219649210002498

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