Sparse feature selection: Relevance, redundancy and locality structure preserving guided by pairwise constraints

Zahir Noorie; Fatemeh Afsari

doi:10.1016/j.asoc.2019.105956

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Sparse feature selection: Relevance, redundancy and locality structure preserving guided by pairwise constraints

Zahir Noorie Fatemeh Afsari

Year: 2019 Journal: Applied Soft Computing Vol: 87 Pages: 105956-105956 Publisher: Elsevier BV

DOI: 10.1016/j.asoc.2019.105956

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Keywords:

Laplacian matrix Computer science Locality Pairwise comparison Discriminative model Feature selection Minimum redundancy feature selection Artificial intelligence Redundancy (engineering) Graph Dense graph Machine learning Pattern recognition (psychology) Data mining Theoretical computer science

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Sparse feature selection: Relevance, redundancy and locality structure preserving guided by pairwise constraints

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