Feature co-shrinking for co-clustering

Qi Tan, Pei Yang, Jingrui He

Abstract: 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. Abstract Many real-world applications require multi-way feature selection rather than single-way feature selection. Multi-way feature selection is more challenging compared to single-way feature selection due to the presence of inter-correlation among the multi-way features. To address this challenge, we propose a novel non-negative matrix tri-factorization model based on co-sparsity regularization to facilitate feature co-shrinking for co-clustering. The basic idea is to learn the inter-correlation among the multi-way features while shrinking the irrelevant ones by encouraging the co-sparsity of the model parameters. The objective is to simultaneously minimize the loss function for the matrix tri-factorization, and the co-sparsity regularization imposed on the model. Furthermore, we develop an efficient and convergence-guaranteed algorithm to solve the non-smooth optimization problem, which works in an iteratively update fashion. The experimental results on various data sets demonstrate the effectiveness of the proposed approach.