What is it about?
A new dimension reduction method that incorporates auxiliary information and accommodates special features of data.
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Why is it important?
One of the most flexible form of PCA so far, incorporating supervision, sparsity and smoothness simultaneously, containing almost all existing variants of PCA as special cases.
Perspectives
Very flexible and easy to use. A good data exploratory tool.
Dr Gen Li
Columbia University
Read the Original
This page is a summary of: Supervised Sparse and Functional Principal Component Analysis, Journal of Computational and Graphical Statistics, July 2016, Taylor & Francis,
DOI: 10.1080/10618600.2015.1064434.
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