High-throughput data dimension reduction via seeded canonical correlation analysis
- 주제(키워드) Canonical correlation analysis , Large p small n , Multivariate analysis , Seeded dimension reduction
- 등재 SCIE, SCOPUS
- 발행기관 John Wiley and Sons Ltd
- 발행년도 2015
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000114166
- 본문언어 영어
- Published As http://dx.doi.org/10.1002/cem.2691
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
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.
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