To rectify the situation, we first develop several heuristic deflation alternatives with more desirable properties. We then reformulate the sparse PCA ...
In this work, we demon- strate that the standard PCA deflation procedure is seldom appropriate for the sparse PCA setting. To rectify the situation, we first ...
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Sparse PCA through Low-rank Approximations ... We introduce a novel algorithm that computes the $k$-sparse principal component of a positive semidefinite matrix $ ...
Dec 8, 2008 · To rectify the situation, we first develop several deflation alternatives better suited to the cardinality-constrained context. We then ...
Dec 3, 2019 · This method keeps a good tradeoff between various criteria, including sparsity, orthogonality, explained variance, balance of sparsity, and ...
Aug 30, 2017 · Introduction. Principal component analysis (PCA) is a popular change of variables technique used in dimension reduction and visualization.
Witten et al. (2009) introduce a framework known as the penalized matrix decomposition. (PMD), which uses low-rank approximations of the data matrix to ...
In this paper we proposed an iterative elimination algorithm for sparse principal component anal- ysis. It recursively eliminates variables according to ...
Abstract. Modern treatments of structured Principal Component Anal- ysis often focus on the estimation of a single component.
Dec 26, 2020 · In this Part II we provide a thorough analysis of the limitations of sPCA methods that use deflation for calculating subsequent, higher order, ...