Dec 23, 2020 · Abstract: An iterative method for the computation of a non-negative sparse principal component basis is presented.
1) A novel method is proposed that computes non-negative and sparse basis by imposing only non-negativity con- straints (see Section V-A). 2) The proposed ...
Due to the non-negative constraints explicitly enforced, the extracted eigenvectors turn to be sparse without employing any sparsity controlling parameter.
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What is a sparse PCA?
We describe a nonnegative variant of the Sparse PCA problem. The goal is to create a low dimensional representation from a collection of points.
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We study the problem of finding the dominant eigenvector of the sample covariance matrix, under additional constraints on the vector.
We present and analyze a simple, two-step algorithm to approximate the optimal solution of the sparse PCA problem. In the proposed approach, we first solve ...
We describe a nonnegative variant of the Sparse PCA problem. The goal is to create a low dimensional representation from a collection of points.
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Aug 10, 2024 · Furthermore, we employ non-negative sparse matrix approximation techniques to refine the estimates of risk relativities for basic rating units.
With applications throughout science and engineering, sparse principal component analysis considers the problem of maximizing the variance explained by a ...
Recently, some alternatives to the standard PCA approach, such as sparse PCA (SPCA), have been proposed, their aim being to limit the density of the components.