High‐throughput data dimension reduction via seeded canonical correlation analysis. (26th November 2014)

• Record Type: Journal Article
• Authors: Im, Yunju; Gang, HeyIn; Yoo, Jae Keun
• Is Part Of: Journal of chemometrics. Volume 29:Number 3(2015:Mar.)
• Page Start: 193
• Page End: 199
• Publication Date: 2014-11-26
• Subjects: Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.2691
• Languages: English
• ISSNs: 0886-9383

Abstract

<abstract abstract-type="main" id="cem2691-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="cem2691-para-0001">Canonical correlation analysis (CCA) is one of popular statistical methodologies in multivariate analysis, especially, in studying relation of two sets of variables. However, if sample sizes are smaller than the maximum of the dimensions of two sets of variables, it is not plausible to construct canonical coefficient matrices due to failure of inverting sample covariance matrices. In this article, we develop a two step procedure of CCA implemented in such situation. For this, seeded dimension reduction is adapted into CCA. Numerical studies confirm the approach, and two real data analyses are presented. Copyright © 2014 John Wiley &amp; Sons, Ltd.</p> </abstract>