Regularized Multiple-Set Canonical Correlation Analysis

Autor/inn/en	Takane, Yoshio; Hwang, Heungsun; Abdi, Herve
Titel	Regularized Multiple-Set Canonical Correlation Analysis
Quelle	In: Psychometrika, 73 (2008) 4, S.753-775 (23 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-008-9065-0
Schlagwörter	Prior Learning; Multivariate Analysis; Correlation; Data Analysis; Evaluation Methods + Suchen Sie Ihr Suchwort? Vorkenntnisse; Multivariate Analyse; Korrelation; Auswertung
Abstract	Multiple-set canonical correlation analysis (Generalized CANO or GCANO for short) is an important technique because it subsumes a number of interesting multivariate data analysis techniques as special cases. More recently, it has also been recognized as an important technique for integrating information from multiple sources. In this paper, we present a simple regularization technique for GCANO and demonstrate its usefulness. Regularization is deemed important as a way of supplementing insufficient data by prior knowledge, and/or of incorporating certain desirable properties in the estimates of parameters in the model. Implications of regularized GCANO for multiple correspondence analysis are also discussed. Examples are given to illustrate the use of the proposed technique. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10