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• W2428936319 abstract "Block principal component analysis with l1 -norm (BPCA-L1) has demonstrated its effectiveness in a lot of visual classification and data mining tasks. However, the greedy strategy for solving the l1 -norm maximization problem is prone to being struck in local solutions. In this paper, we propose a BPCA with nongreedy l1 -norm maximization, which obtains better solutions than BPCA-L1 with all the projection directions optimized simultaneously. Other than BPCA-L1, the new algorithm has been evaluated against some popular principal component analysis (PCA) algorithms including PCA-L1 and 2-D PCA-L1 on a variety of benchmark data sets. The results demonstrate the effectiveness of the proposed method." @default.
• W2428936319 created "2016-06-24" @default.
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• W2428936319 date "2016-11-01" @default.
• W2428936319 modified "2023-10-15" @default.
• W2428936319 title "Block Principal Component Analysis With Nongreedy $ell _{1}$ -Norm Maximization" @default.
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• W2428936319 doi "https://doi.org/10.1109/tcyb.2015.2479645" @default.
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