High-throughput data dimension reduction via seeded canonical correlation analysis

Yunju Im, Heyin Gang, Jae Keun Yoo

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)193-199
Number of pages7
JournalJournal of Chemometrics
Volume29
Issue number3
DOIs
StatePublished - 1 Mar 2015

Bibliographical note

Publisher Copyright:
© 2014 John Wiley and Sons, Ltd.

Keywords

  • Canonical correlation analysis
  • Large p small n
  • Multivariate analysis
  • Seeded dimension reduction

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