High-throughput data dimension reduction via seeded canonical correlation analysis

Yunju Im; Heyin Gang; Jae Keun Yoo

doi:10.1002/cem.2691

High-throughput data dimension reduction via seeded canonical correlation analysis

Yunju Im, Heyin Gang, Jae Keun Yoo

Department of Statistics

Research output: Contribution to journal › Article › peer-review

5 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 language	English
Pages (from-to)	193-199
Number of pages	7
Journal	Journal of Chemometrics
Volume	29
Issue number	3
DOIs	https://doi.org/10.1002/cem.2691
State	Published - 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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10.1002/cem.2691

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High-throughput data dimension reduction via seeded canonical correlation analysis

Abstract

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Keywords

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