An exact approach to sparse principal component analysis

被引：0
作者：
Alessio Farcomeni
机构：
[1] Università di Roma “La Sapienza”,
来源：
Computational Statistics | 2009年 / 24卷
关键词：
Branch and bound; Dimension reduction; Feature selection; Feature extraction; Interleaving eigenvalues theorem; Sparse principal components;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We show a branch and bound approach to exactly find the best sparse dimension reduction of a matrix. We can choose between enforcing orthogonality of the coefficients and uncorrelation of the components, and can explicitly set the degree of sparsity. We suggest methods to choose the number of non-zero loadings for each component; illustrate and compare our approach with existing methods through a benchmark data set.
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页码：583 / 604
页数：21
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