Subspace learning for unsupervised feature selection via adaptive structure learning and rank approximation

Ronghua Shang; Kaiming Xu; Licheng Jiao

doi:10.1016/j.neucom.2020.06.111

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Subspace learning for unsupervised feature selection via adaptive structure learning and rank approximation

Ronghua Shang Kaiming Xu Licheng Jiao

Year: 2020 Journal: Neurocomputing Vol: 413 Pages: 72-84 Publisher: Elsevier BV

DOI: 10.1016/j.neucom.2020.06.111

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Keywords:

Pattern recognition (psychology) Artificial intelligence Subspace topology Computer science Laplacian matrix Feature (linguistics) Matrix decomposition Unsupervised learning Nonlinear dimensionality reduction Feature selection Mathematics Algorithm Dimensionality reduction Laplace operator Eigenvalues and eigenvectors

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Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Image Retrieval and Classification Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Remote Sensing and Land Use

Physical Sciences → Earth and Planetary Sciences → Atmospheric Science

Subspace learning for unsupervised feature selection via adaptive structure learning and rank approximation

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