Robust nonnegative matrix factorization via joint graph Laplacian and discriminative information for identifying differentially expressed genes

DOI10.1155/2017/4216797zbMath1367.92053OpenAlexW2604604725WikidataQ59142901 ScholiaQ59142901MaRDI QIDQ2012798

Jin-Xing Liu, Chun-Mei Feng, Ling-Yun Dai, Mi-Xiao Hou, Jiguo Yu, Chun-Hou Zheng

Publication date: 3 August 2017

Published in: Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2017/4216797

zbMATH Keywords

classificationcancer diagnosisgraph Laplacian and discriminative information (GLD-RNMF)robust nonnegative matrix factorization

Mathematics Subject Classification ID

Factorization of matrices (15A23) Applications of graph theory (05C90) Medical applications (general) (92C50) Genetics and epigenetics (92D10)

Related Items (2)

A new robust classifier on noise domains: bagging of Credal C4.5 trees ⋮ Joint nonnegative matrix factorization based on sparse and graph Laplacian regularization for clustering and co-differential expression genes analysis

Cites Work

• Clustering and feature selection using sparse principal component analysis
• Solving quasi-variational inequalities via their KKT conditions
• Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent
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