Directly solving normalized cut for multi-view data

Graph-based multi-view clustering, which aims to uncover clusters from multi-view data with graph clustering technique, is one of the most important multi-view clustering methods. Such methods usually perform eigen-decomposition first to solve the relaxed problem and then obtain the final cluster indicator matrix from eigenvectors by k-means or spectral rotation. However, such a two-step process may result in undesired clustering result since the two steps aim to solve different problems. In this paper, we propose a k-way normalized cut method for multi-view data, named as the Multi-view Discrete Normalized Cut (MDNC). The new method learns a set of implicit weights for each view to identify its quality, and a novel iterative algorithm is proposed to directly solve the new model without relaxation and post-processing. Moreover, we propose a new method to adjust the distribution of the implicit view weights to obtain better clustering result. Extensive experimental results show that the performance of our approach is superior to the state-of-the-art methods.