Functional logistic discrimination with sparse PCA and its application to the structural MRI
Sign Up to like & getrecommendations! Published in 2019 at "Behaviormetrika"
DOI: 10.1007/s41237-019-00079-3
Abstract: We propose a functional classification method with high-dimensional image predictors using a combination of logistic discrimination and basis expansions with sparse principal component analysis (PCA). Our model is an extension of the existing functional generalized… read more here.
Keywords: pca application; functional logistic; logistic discrimination; discrimination sparse ... See more keywords
All sparse PCA models are wrong, but some are useful. Part II: Limitations and problems of deflation
Sign Up to like & getrecommendations! Published in 2020 at "Chemometrics and Intelligent Laboratory Systems"
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