Multi-view clustering with extreme learning machine.pdfVIP

Neurocomputing 214 (2016) 483–494 Contents lists available at ScienceDirect Neurocomputing journal homepage: /locate/neucom Multi-view clustering with extreme learning machine Qiang Wang a,b,n, Yong Dou a,b, Xinwang Liu a,b, Qi Lv a,b, Shijie 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 24 November 2015 Received in revised form 24 May 2016 Accepted 16 June 2016 Communicated by G.-B. Huang Available online 23 June 2016 Keywords: Multi-view clustering Unsupervised clustering Extreme learning machine abstract Nowadays, data always have multiple representations, and a good feature representation usually leads to a good clustering performance. Existing multi-view clustering works generally integrate multiple complementary information to gain better clustering performance rather than relying on a single view. However, these works usually focus on the combination of information rather than improving the feature representation capability of each view. As a new method, extreme learning machine (ELM) has excellent feature representation capability, easy parameter selection, and promising performance in various clustering tasks. This paper proposes a novel multi-view clustering framework with ELM to further improve clustering performance, and implements three algorithms based on this framework. In this framework, the normalized features of each individual view are mapped onto a higher dimensional feature space by the ELM random mapping. Afterwards, the unsupervised multi-view clustering is performed in this feature space. Thus far, this is the ?rst work on multi-view clustering with ELM. Numerous baseline methods on ?ve real-world datasets are empirically compared to show the effectiveness of the proposed algorithms. As indicated, the proposed algorithms yield superior cluste