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Clustering and feature selection using sparse principal component analysis

Luss, Ronny / d’Aspremont, Alexandre

Abstract In this paper, we study the application of sparse principal component analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combinations of the data variables, explaining a maximum amount of variance in the data while having only a limited number of nonzero coefficients. PCA is often used as a simple clustering technique and sparse factors allow us here to interpret the clusters in terms of a reduced set of variables. We begin with a brief introduction and motivation on sparse PCA and detail our implementation of the algorithm in d’Aspremont et al. (SIAM Rev. 49(3):434–448, 2007). We then apply these results to some classic clustering and feature selection problems arising in biology.

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Clustering and feature selection using sparse principal component analysis

Luss, Ronny / d’Aspremont, Alexandre

Abstract In this paper, we study the application of sparse principal component analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combinations of the data variables, explaining a maximum amount of variance in the data while having only a limited number of nonzero coefficients. PCA is often used as a simple clustering technique and sparse factors allow us here to interpret the clusters in terms of a reduced set of variables. We begin with a brief introduction and motivation on sparse PCA and detail our implementation of the algorithm in d’Aspremont et al. (SIAM Rev. 49(3):434–448, 2007). We then apply these results to some classic clustering and feature selection problems arising in biology.

Clustering and feature selection using sparse principal component analysis

Luss, Ronny / d’Aspremont, Alexandre

Abstract In this paper, we study the application of sparse principal component analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combinations of the data variables, explaining a maximum amount of variance in the data while having only a limited number of nonzero coefficients. PCA is often used as a simple clustering technique and sparse factors allow us here to interpret the clusters in terms of a reduced set of variables. We begin with a brief introduction and motivation on sparse PCA and detail our implementation of the algorithm in d’Aspremont et al. (SIAM Rev. 49(3):434–448, 2007). We then apply these results to some classic clustering and feature selection problems arising in biology.

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Title:

Clustering and feature selection using sparse principal component analysis

Contributors:

Luss, Ronny (author) / d’Aspremont, Alexandre (author)

Published in:

Optimization and Engineering ; 11 ; 145-157

Publication date:

2008-11-06

Size:

13 pages

ISSN:

1573-2924 , 1389-4420

DOI:

https://doi.org/10.1007/s11081-008-9057-z

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

English

Keywords:

Sparse principal component analysis , Semidefinite programming , Clustering , Feature selection Mathematics , Optimization , Engineering, general , Systems Theory, Control , Environmental Management , Operation Research/Decision Theory , Financial Engineering

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