Incomplete multi-view clustering via deep semantic mapping

Abstract

Abstract Multi-view clustering, which explores complementary information between multiple feature sets by consensus grouping, has benefited many data analytic applications. The majority of previous multi-view clustering studies usually assume that all feature sets appear in complete. In real-world applications, however, it is often the case that some views could suffer from the missing of examples, resulting in incomplete feature sets. The incompleteness of views makes it difficult to synthesize all feature sets and achieve a comprehensive description of data samples. In this paper, we develop a novel incomplete multi-view clustering method, which projects all incomplete multi-view data to a complete and unified representation in a common subspace. Different from existing researches which exploit shallow learning to identify the common subspace, a deep incomplete multi-view clustering (DIMC) incorporating with the constraint of intrinsic geometric structure is proposed here to couple incomplete multi-view samples. To bridge the gap between each view and the common representation, the multi-view deep coupled networks are trained to map high-level semantic features. Besides, to preserve the local invariance for each view, an affinity graph based regularizer is constructed to encode geometrical information. Therefore, a new objective function is developed and the optimization processes are presented. Extensive experiments on several real-world datasets demonstrate that the proposed DIMC is superior to the state-of-the-art incomplete multi-view clustering methods.