Component retention in principal component analysis with application to cDNA microarray data

被引：0
|
作者：
Richard Cangelosi
Alain Goriely
机构：
[1] University of Arizona,Department of Mathematics
[2] University of Arizona,Program in Applied Mathematics
[3] University of Arizona,BIO5 Institute
来源：
Biology Direct | / 2卷
关键词：
Principal Component Analysis; Singular Value Decomposition; Information Dimension; True Dimension; Break Stick;
D O I：
暂无
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学科分类号：
摘要：
Shannon entropy is used to provide an estimate of the number of interpretable components in a principal component analysis. In addition, several ad hoc stopping rules for dimension determination are reviewed and a modification of the broken stick model is presented. The modification incorporates a test for the presence of an "effective degeneracy" among the subspaces spanned by the eigenvectors of the correlation matrix of the data set then allocates the total variance among subspaces. A summary of the performance of the methods applied to both published microarray data sets and to simulated data is given.
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