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

• Type: Journal
• Year: 2007
• Where: CORR
• Authors: Ronny Luss, Alexandre d'Aspremont
• Keywords: CORR 2007, Education, Sparse, Sparse PCA, Sparse Principal Component