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• W2063715296 abstract "The 2-D principal component analysis (2-DPCA) is a widely used method for image feature extraction. However, it can be equivalently implemented via image-row-based principal component analysis. This paper presents a structured 2-D method called nuclear norm-based 2-DPCA (N-2-DPCA), which uses a nuclear norm-based reconstruction error criterion. The nuclear norm is a matrix norm, which can provide a structured 2-D characterization for the reconstruction error image. The reconstruction error criterion is minimized by converting the nuclear norm-based optimization problem into a series of F-norm-based optimization problems. In addition, N-2-DPCA is extended to a bilateral projection-based N-2-DPCA (N-B2-DPCA). The virtue of N-B2-DPCA over N-2-DPCA is that an image can be represented with fewer coefficients. N-2-DPCA and N-B2-DPCA are applied to face recognition and reconstruction and evaluated using the Extended Yale B, CMU PIE, FRGC, and AR databases. Experimental results demonstrate the effectiveness of the proposed methods." @default.
• W2063715296 created "2016-06-24" @default.
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• W2063715296 date "2015-10-01" @default.
• W2063715296 modified "2023-10-18" @default.
• W2063715296 title "Nuclear Norm-Based 2-DPCA for Extracting Features From Images" @default.
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• W2063715296 doi "https://doi.org/10.1109/tnnls.2014.2376530" @default.
• W2063715296 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25585426" @default.
• W2063715296 hasPublicationYear "2015" @default.
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