Consistency of sparse PCA in High Dimension, Low Sample Size contexts
نویسندگان
چکیده
منابع مشابه
Consistency of sparse PCA in High Dimension, Low Sample Size contexts
Sparse Principal Component Analysis (PCA) methods are efficient tools to reduce the dimension (or number of variables) of complex data. Sparse principal components (PCs) are easier to interpret than conventional PCs, because most loadings are zero. We study the asymptotic properties of these sparse PC directions for scenarios with fixed sample size and increasing dimension (i.e. High Dimension,...
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Principal Component Analysis (PCA) is an important tool of dimension reduction especially when the dimension (or the number of variables) is very high. Asymptotic studies where the sample size is fixed, and the dimension grows (i.e. High Dimension, Low Sample Size (HDLSS)) are becoming increasingly relevant. We investigate the asymptotic behavior of the Principal Component (PC) directions. HDLS...
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ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2013
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2012.10.007