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- W2886014585 abstract "Locality preserving projection (LPP) is a classical tool for dimensionality reduction and feature extraction. It usually makes use of the ℓ2-norm criterion for optimization, and is thus sensitive to outliers. In order to achieve robustness, LPP-L1 is proposed by employing the ℓ1-norm as distance criterion. However, the edge weights of LPP-L1 measure only the dissimilarity of pairs of vertices and ignore the preservation of the similarity. In this paper, we develop a novel algorithm, termed as ILPP-L1, in which the ℓ1-norm is utilized to obtain robustness and the similarities of pairs of vertices are effectively preserved, simultaneously. ILPP-L1 is robust to outliers because of the use of the ℓ1-norm. The ℓ1-norm minimization problem is directly solved, which ensures the preservation of the similarity of pairs of vertices. The solution is justified to converge to local minimum. In addition, ILPP-L1 avoids small sample size problem. Experiment results on benchmark databases confirm the effectiveness of the proposed method." @default.
- W2886014585 created "2018-08-22" @default.
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- W2886014585 date "2018-11-01" @default.
- W2886014585 modified "2023-10-17" @default.
- W2886014585 title "An improved locality preserving projection with ℓ1-norm minimization for dimensionality reduction" @default.
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- W2886014585 doi "https://doi.org/10.1016/j.neucom.2018.08.008" @default.
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