A novel multi-view clustering method via low-rank and matrix-induced regularization.pdfVIP

Neurocomputing 216 (2016) 342–350 Contents lists available at ScienceDirect Neurocomputing journal homepage: /locate/neucom A novel multi-view clustering method via low-rank and matrix-induced regularization Yang Zhao a,b,n, Yong Dou a,b, Xinwang Liu a,b, Teng Li a,b a National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China b College of Computer, National University of Defense Technology, Changsha, China article info Article history: Received 14 March 2016 Received in revised form 18 July 2016 Accepted 3 August 2016 Communicated by Jun Yu Available online 8 August 2016 Keywords: Multi-view clustering Unsupervised clustering Robustness Diversity Matrix-induced regularization abstract Multi-view clustering algorithms have shown promising performance in various applications over the last few decades. Most of them, however, do not adequately take noises and correlation among multiple views into account, which may degrade the clustering performance. In this paper, we propose a novel multi-view clustering method to address these issues. In speci?c, we construct a low-rank consensus matrix and a sparse error matrix from each similarity matrix corresponding to each view. Furthermore, a matrix-induced regularization term is incorporated to reduce the redundancy and enhance the diversity among different views. The augmented Lagrangian multiplier algorithm is adopted to solve the resultant optimization problem. Comprehensive experiments are conducted to verify the effectiveness of the proposed algorithm. Results demonstrate that our algorithm outperforms several state-of-the-art ones on both synthetic and benchmark data sets. 2016 Elsevier B.V. All rights reserved. 1. Introduction Multi-view clustering, which aims to partition data points into groups with similar ones by applying information from different views, has shown great performance in recent years. Multi-view data are common in real-life problems. For ex