DSpace at EWHA: High-throughput data dimension reduction via seeded canonical correlation analysis

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DSpace at EWHA자연과학대학 통계학전공 Journal papers

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High-throughput data dimension reduction via seeded canonical correlation analysis

Title: High-throughput data dimension reduction via seeded canonical correlation analysis

Authors: Im Y.; Gang H.; Yoo J.K.

Ewha Authors: 유재근

SCOPUS Author ID: 유재근

Issue Date: 2014

Journal Title: Journal of Chemometrics

ISSN: 0886-9383

Citation: Journal of Chemometrics

Publisher: John Wiley and Sons Ltd

Indexed: SCI; SCIE; SCOPUS

Document Type: Article in Press

Abstract: Canonical correlation analysis; Large p small n; Multivariate analysis; Seeded dimension reduction

DOI: 10.1002/cem.2691

Appears in Collections:: 자연과학대학 > 통계학전공 > Journal papers

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