All sparse PCA models are wrong, but some are useful. Part II: Limitations and problems of deflation
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DOI: 10.1016/j.chemolab.2020.104212
Abstract: Abstract Sparse Principal Component Analysis (sPCA) is a popular matrix factorization approach based on Principal Component Analysis (PCA). It combines variance maximization and sparsity with the ultimate goal of improving data interpretation. A main application… read more here.
Keywords: pca models; part; analysis; wrong useful ... See more keywords