Multi-view clustering with extreme learning machine

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 first work on multi-view clustering with ELM. Numerous baseline methods on five real-world datasets are empirically compared to show the effectiveness of the proposed algorithms. As indicated, the proposed algorithms yield superior clustering performance when compared with several state-of-art multi-view clustering methods in recent literatures.