Multi-view clustering via joint feature selection and partially constrained cluster label learning

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Real world data are often represented by multiple distinct feature sets, and some prior knowledge is provided, such as labels of some examples or pairwise constraints between several sample pairs. Accordingly, task of multi-view clustering arises from a complex information aggregation of multiple sources of feature sets and knowledge prior. In this paper, we propose to optimize the cluster indicator, which representing the class labels is an intuitive reflection of the clustering structure. Besides, the prior indicating the same level of semantics can be directly utilized guiding the learned clustering structure. Furthermore, feature selection is embedded into the above process to select views and features in each view, which leads to the most discriminative views and features chosen for every single cluster. To these ends, an objective is accordingly proposed with an efficient optimization strategy and convergence analysis. Extensive experiments demonstrate that our model performs better than the state-of-the-art methods.