General adaptive sparse-PCA for high dimensional data with low sample

Authors

  • Mark Gil Torres Department of Mathematics and Statistics, College of Science and Mathematics, Mindanao State University-Iligan Institute of Technology, 9200 Iligan City, Philippines

Abstract

In this paper, we propose a novel solution to the general adaptive sparse-PCA (GAS-PCA), which was developed by [3], to work with high dimensional data with low sample size (HDDLSS).

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Published

2012-10-01

How to Cite

Torres, M. G. (2012). General adaptive sparse-PCA for high dimensional data with low sample. The Mindanawan Journal of Mathematics, 3(2), 145–154. Retrieved from https://journals.msuiit.edu.ph/tmjm/article/view/8

Issue

Vol. 3 No. 2

Section

Articles