Incomplete Multiview Learning via Wyner Common Information

Abstract: Incomplete multiview clustering is of high recent interest, fueled by the advancement of common information-based deep multiview learning. The practical scenarios where unpaired multiview data with missing values have wide applications in generative learning, cross-modal retrieval, and wireless device identification problems. Following the perspective that the shared information between the incomplete multiview data aligns with the cluster targets, recent works have generalized the well-known common information frameworks in information theory multiview learning problems, with improved performance reported. Different from previous works, we extend the frameworks to incomplete multiview clustering problems and propose an efficient solver: Wyner Incomplete MultiView Clustering (WyIMVC). Interestingly, the common randomness in WyIMVC allows for joint clustering and missing value inference in contrast to the compared methods in the literature. Moreover, leveraging the difference-of-convex structure of the formulated problems, we propose an efficient solver with a convergence guarantee independent of initialization. Empirically, our solver outperforms the state-of-the-art solvers in a range of incomplete multiview datasets with varying numbers of views and dimensions.