Skip to Main content Skip to Navigation
Journal articles

Clustering and Disjoint Principal Component Analysis

Abstract : 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.
Keywords : cluster analysis PCA
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01125538
Contributor : Laboratoire Cedric <>
Submitted on : Friday, March 6, 2015 - 11:13:25 AM
Last modification on : Saturday, March 14, 2020 - 4:30:22 PM

Identifiers

Collections

Citation

Maurizio Vichi, Gilbert Saporta. Clustering and Disjoint Principal Component Analysis. Computational Statistics and Data Analysis, Elsevier, 2008, 53 (8), pp.3194-3208. ⟨10.1016/j.csda.2008.05.028⟩. ⟨hal-01125538⟩

Share

Metrics

Record views

117