Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization
作者：
Highlights：
• Graph has been used to regularize nonnegative matrix factorization (NMF).
• However, noisy features and nonlinear distributed data effect the graph construction.
• We proposed to integrate feature selection and multi-kernel learning to this problem.
• Novel algorithms are developed to learn feature/kernel weights and NMF parameters.
摘要
•Graph has been used to regularize nonnegative matrix factorization (NMF).•However, noisy features and nonlinear distributed data effect the graph construction.•We proposed to integrate feature selection and multi-kernel learning to this problem.•Novel algorithms are developed to learn feature/kernel weights and NMF parameters.
论文关键词：Data representation,Nonnegative matrix factorization,Graph regularization,Feature selection,Multi-kernel learning
论文评审过程：Available online 20 September 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.09.008
