Multi-view clustering aims to leverage information from multiple views to improve clustering. Most previous works assumed that each view has complete data. However, in real-world datasets, it is often the case that a view may contain some missing data, resulting in the incomplete multi-view clustering problem. Previous methods for this problem have at least one of the following drawbacks: (1) employing shallow models, which cannot well handle the dependence and discrepancy among different views; (2) ignoring the hidden information of the missing data; (3) dedicated to the two-view case. To eliminate all these drawbacks, in this work we present an Adversarial Incomplete Multi-view Clustering (AIMC) method. Unlike most existing methods which only learn a new representation with existing views, AIMC seeks the common latent space of multi-view data and performs missing data inference simultaneously. In particular, the element-wise reconstruction and the generative adversarial network (GAN) are integrated to infer the missing data. They aim to capture overall structure and get a deeper semantic understanding respectively. Moreover, an aligned clustering loss is designed to obtain a better clustering structure. Experiments conducted on three datasets show that AIMC performs well and outperforms baseline methods.

Multi-view data is common in real-world datasets, where different views describe distinct perspectives. To better summarize the consistent and complementary information in multi-view data, researchers have proposed various multi-view representation learning algorithms, typically based on factorization models. However, most previous methods were focused on shallow factorization models which cannot capture the complex hierarchical information. Although a deep multi-view factorization model has been proposed recently, it fails to explicitly discern consistent and complementary information in multi-view data and does not consider conceptual labels. In this work we present a semi-supervised deep multi-view factorization method, named Deep Multi-view Concept Learning (DMCL). DMCL performs nonnegative factorization of the data hierarchically, and tries to capture semantic structures and explicitly model consistent and complementary information in multi-view data at the highest abstraction level. We develop a block coordinate descent algorithm for DMCL. Experiments conducted on image and document datasets show that DMCL performs well and outperforms baseline methods.