Joint local structure preservation and redundancy minimization for unsupervised feature selection

Hao Li; Yongli Wang; Yanchao Li; Peng Hu; Ruxin Zhao

doi:10.1007/s10489-020-01800-6

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Joint local structure preservation and redundancy minimization for unsupervised feature selection

Hao Li Yongli Wang Yanchao Li Peng Hu Ruxin Zhao

Year: 2020 Journal: Applied Intelligence Vol: 50 (12)Pages: 4394-4411 Publisher: Springer Science+Business Media

DOI: 10.1007/s10489-020-01800-6

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Keywords:

Computer science Feature selection Redundancy (engineering) Pattern recognition (psychology) Pairwise comparison Artificial intelligence Minimum redundancy feature selection Minification Data mining Feature (linguistics) Benchmark (surveying)

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Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Remote-Sensing Image Classification

Physical Sciences → Engineering → Media Technology

Machine Learning and ELM

Physical Sciences → Computer Science → Artificial Intelligence

Joint local structure preservation and redundancy minimization for unsupervised feature selection

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