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

Author:

Aswani Kumar Ch.1,Palanisamy Ramaraj2

Affiliation:

1. Networks and Information Security Division, School of Information Technology and Engineering, VIT University, Vellore 632014, India

2. Department of Information Systems, St. Francis Xavier University, Canada

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.

Publisher

World Scientific Pub Co Pte Lt

Subject

Library and Information Sciences,Computer Networks and Communications,Computer Science Applications

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Intellectual Structure of Knowledge Management: A Bibliometric Analysis of the Journal of Information and Knowledge Management;Journal of Information & Knowledge Management;2021-12-02

2. Semi-Discrete Decomposition;Encyclopedia of Social Network Analysis and Mining;2018

3. Semi-Discrete Decomposition;Encyclopedia of Social Network Analysis and Mining;2016

4. Semi-discrete Decomposition;Encyclopedia of Social Network Analysis and Mining;2014

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