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• W2952492777 abstract "The computation of the sparse principal component of a matrix is equivalent to the identification of its principal submatrix with the largest maximum eigenvalue. Finding this optimal submatrix is what renders the problem ${mathcal{NP}}$-hard. In this work, we prove that, if the matrix is positive semidefinite and its rank is constant, then its sparse principal component is polynomially computable. Our proof utilizes the auxiliary unit vector technique that has been recently developed to identify problems that are polynomially solvable. Moreover, we use this technique to design an algorithm which, for any sparsity value, computes the sparse principal component with complexity ${mathcal O}left(N^{D+1}right)$, where $N$ and $D$ are the matrix size and rank, respectively. Our algorithm is fully parallelizable and memory efficient." @default.
• W2952492777 created "2019-06-27" @default.
• W2952492777 creator A5018630988 @default.
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• W2952492777 date "2013-12-20" @default.
• W2952492777 modified "2023-09-27" @default.
• W2952492777 title "The Sparse Principal Component of a Constant-rank Matrix" @default.
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• W2952492777 hasPublicationYear "2013" @default.
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