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CORR
2007
Springer

Clustering and Feature Selection using Sparse Principal Component Analysis

8 years 3 months ago
Clustering and Feature Selection using Sparse Principal Component Analysis
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
Ronny Luss, Alexandre d'Aspremont
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2007
Where CORR
Authors Ronny Luss, Alexandre d'Aspremont
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