Robust Discriminative multi-view K-means clustering with feature selection and group sparsity learning

With the rapid development of information technologies, more and more data are collected from multiple sources, which contain different perspectives of the data. To accurately explore the shared information among multiple views, K-means based multi-view clustering methods are designed and widely used in various applications for their simplicity and efficiency. However, all of these methods cluster data in the original high-dimensional feature space which is extremely time-consuming and sensitive to outliers, or cluster data in the embedded feature space for each view, which is hard to find the optimal reduced dimensionality. To solve these problems, we propose a robust discriminative multi-view K-means clustering with feature selection and group sparsity learning. Compared to the state-of-the-arts, the proposed algorithm has two advantages: 1) Discriminative K-means clustering and feature learning are integrated jointly into a single framework, where robust and accurate clustering results are obtained in the embedded feature space with an l2, 1-norm based loss function. 2) Group sparsity constraints are imposed to select the most relevant features and the most important views. We apply the proposed algorithm to serval kinds of multimedia understanding applications. Experimental results demonstrate the effectiveness of the proposed algorithm.