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- W4281261674 abstract "Subspace clustering is a powerful technology for clustering high-dimensional data. Least squares regression (LSR) is one of the classical subspace clustering models because of the explicit solution and grouping effect that tends to cluster highly correlated data together. However, LSR lacks sparsity, which may lead to its sensitivity to noise and dimension of data. Since the ℓ0-norm constrained optimization is difficult to solve, we introduce a new method based on the 0-1 integer programming, named k-sparse LSR (KSLSR), to directly solve the optimization of the LSR constrained by an ℓ0-norm-based constraint without any relaxation, where k represents the maximum number of non-zero elements of the representation coefficient. With the help of the 0-1 integer constraint, the ℓ0-norm-based constraint is equivalent to two continuous constraints. Thus, the obtained KSLSR method with two continuous constraints is equivalent to the ℓ0-norm constrained optimization and can be effectively solved by the alternating direction multiplier method (ADMM). In addition, the new method is proven to provide a solution with both k-sparsity and the grouping effect, which makes it robust to the noise and dimension of data. Extensive experiments are conducted to demonstrate the effectiveness and superiority of the proposed method." @default.
- W4281261674 created "2022-05-23" @default.
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- W4281261674 date "2022-10-01" @default.
- W4281261674 modified "2023-10-14" @default.
- W4281261674 title "The <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML altimg=si4.svg><mml:mi>k</mml:mi></mml:math>-sparse LSR for subspace clustering via 0-1 integer programming" @default.
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- W4281261674 doi "https://doi.org/10.1016/j.sigpro.2022.108622" @default.
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