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• W2993156957 abstract "The implementation of conventional sparse principal component analysis (SPCA) on high-dimensional data sets has become a time consuming work. In this paper, a series of subspace projections are constructed efficiently by using Household QR factorization. With the aid of these subspace projections, a fast deflation method, called SPCA-SP, is developed for SPCA. This method keeps a good tradeoff between various criteria, including sparsity, orthogonality, explained variance, balance of sparsity, and computational cost. Comparative experiments on the benchmark data sets confirm the effectiveness of the proposed method." @default.
• W2993156957 created "2019-12-13" @default.
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• W2993156957 date "2019-12-03" @default.
• W2993156957 modified "2023-10-15" @default.
• W2993156957 title "A Fast deflation Method for Sparse Principal Component Analysis via Subspace Projections" @default.
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