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

Submitted by Aswani Kumar Ch... on Sat, 2010-06-19 08:05

Publication Type:

Journal Article

Authors:

Kumar, Ch. Aswani; Palanisamy, Ramaraj

Source:

Journal of Information & Knowledge Management (JIKM), Volume 09, Number 01, p.81-92 (2010)

URL:

http://econpapers.repec.org/RePEc:wsi:jikmxx:v:09:y:2010:i:01:p:81-92

Keywords:

Data mining, dimensionality reduction, principal component analysis, Semi Discrete Decomposition, Singular Value Decomposition

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.