Feature co-shrinking for co-clustering
作者：
Highlights：
• We propose a novel non-negative matrix tri-factorization model based on cosparsity regularization to enable the co-feature-selection for co-clustering. It aims to learn the inter-correlation among the multi-way features while co-shrinking the irrelevant ones by encouraging the co-sparsity of the model parameters.
• We propose an efficient algorithm to solve the non-smooth optimization problem. It works in an iteratively update fashion, and is guaranteed to converge.
• Experimental results on various data sets show the effectiveness of the proposed approach.
摘要
•We propose a novel non-negative matrix tri-factorization model based on cosparsity regularization to enable the co-feature-selection for co-clustering. It aims to learn the inter-correlation among the multi-way features while co-shrinking the irrelevant ones by encouraging the co-sparsity of the model parameters.•We propose an efficient algorithm to solve the non-smooth optimization problem. It works in an iteratively update fashion, and is guaranteed to converge.•Experimental results on various data sets show the effectiveness of the proposed approach.
论文关键词：Co-clustering,Non-negative matrix tri-factorization,Co-sparsity,Co-feature-selection
论文评审过程：Received 4 July 2017, Revised 30 October 2017, Accepted 9 December 2017, Available online 11 December 2017, Version of Record 27 December 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.12.005
