Re-Weighted Discriminatively Embedded $K$ -Means for Multi-View Clustering

Recent years, more and more multi-view data are widely used in many real-world applications. This kind of data (such as image data) is high dimensional and obtained from different feature extractors, which represents distinct perspectives of the data. How to cluster such data efficiently is a challenge. In this paper, we propose a novel multi-view clustering framework, called re-weighted discriminatively embedded $K$ -means, for this task. The proposed method is a multi-view least-absolute residual model, which induces robustness to efficiently mitigates the influence of outliers and realizes dimension reduction during multi-view clustering. Specifically, the proposed model is an unsupervised optimization scheme, which utilizes iterative re-weighted least squares to solve least-absolute residual and adaptively controls the distribution of multiple weights in a re-weighted manner only based on its own low-dimensional subspaces and a common clustering indicator matrix. Furthermore, theoretical analysis (including optimality and convergence analysis) and the optimization algorithm are also presented. Compared with several state-of-the-art multi-view clustering methods, the proposed method substantially improves the accuracy of the clustering results on widely used benchmark data sets, which demonstrates the superiority of the proposed work.