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[VS08] Clustering and Disjoint Principal Component Analysis
Revue Internationale avec comité de lecture : Journal Computational Statistics & Data Analysis, vol. 53(8), pp. 3194-3208, 2008, (doi:10.1016/j.csda.2008.05.028)Mots clés: PCA, cluster analysis
Résumé:
A constrained principal component analysis, which aims at a simultaneous clustering of
objects and a partitioning of variables is proposed. The new methodology allows to identify
components with maximum variance, each one a linear combination of a subset of variables. All the
subsets form a partition of variables. Simultaneously, a partition of objects is also computed
maximizing the between cluster variance. The methodology is formulated in a semi-parametric
least-squares framework as a quadratic mixed continuous and integer problem. An alternating leastsquares
algorithm is proposed to solve the clustering and disjoint PCA. Two applications are given
to show the features of the methodology.
Equipe:
msdma
BibTeX
| @article { | |||
| VS08, | |||
| title | = | "{Clustering and Disjoint Principal Component Analysis}", | |
| author | = | "M. Vichi and G. Saporta", | |
| journal | = | "Computational Statistics & Data Analysis", | |
| year | = | 2008, | |
| volume | = | 53, | |
| number | = | 8, | |
| pages | = | "3194-3208", | |
| doi | = | "10.1016/j.csda.2008.05.028", | |
| } | |||
