Discriminatively Constrained Semi-supervised Multi-view Nonnegative Matrix Factorization with Graph Regularization
Abstract
In recent years, semi-supervised multi-view nonnegative matrix factorization (MVNMF) algorithms have achieved promising performances for multi-view clustering. While most of semi-supervised MVNMFs have failed to effectively consider discriminative information among clusters and feature alignment from multiple views simultaneously. In this paper, a novel Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization (DCS^2MVNMF) is proposed. Specifically, a discriminative weighting matrix is introduced for the auxiliary matrix of each view, which enhances the inter-class distinction. Meanwhile, a new graph regularization is constructed with the label and geometrical information. In addition, we design a new feature scale normalization strategy to align the multiple views and complete the corresponding iterative optimization schemes. Extensive experiments conducted on several real world multi-view datasets have demonstrated the effectiveness of the proposed method.
Cite
@article{arxiv.2010.13297,
title = {Discriminatively Constrained Semi-supervised Multi-view Nonnegative Matrix Factorization with Graph Regularization},
author = {Guosheng Cui and Ruxin Wang and Dan Wu and Ye Li},
journal= {arXiv preprint arXiv:2010.13297},
year = {2020}
}