Clustering and feature selection using sparse principal component analysis
نویسندگان
چکیده
منابع مشابه
Clustering and Feature Selection using Sparse Principal Component Analysis
In this paper, we use sparse principal component analysis (PCA) to solve 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 int...
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ژورنال
عنوان ژورنال: Optimization and Engineering
سال: 2008
ISSN: 1389-4420,1573-2924
DOI: 10.1007/s11081-008-9057-z