Multi-view Subspace Clustering via Partition Fusion

Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way. Among various methods, the multi-view subspace clustering approach has gained increasing attention due to its encouraging performance. Basically, it integrates multi-view information into graphs, which are then fed into spectral clustering algorithm for final result. Orthogonal to current work, we propose to fuse multi-view information in a partition space, which enhances the robustness of Multi-view clustering. Specifically, we generate multiple partitions and integrate them to find the shared partition. The proposed model unifies graph learning, generation of basic partitions, and view weight learning. We have conducted comprehensive experiments on benchmark datasets and our empirical results verify the effectiveness and robustness of our approach. Introduction In many real-world problems, data are collected from different sources in diverse domains or described by various feature collectors [1, 2, 3, 4, 5]. To process these kinds of data, a number of multi-view learning algorithms have been developed [8, 9, 10, 11, 12].