Multi-view clustering via adversarial view embedding and adaptive view fusion

Abstract

Multi-view clustering, which explores complementarity and consistency among multiple distinct feature sets to boost clustering performance, is becoming more and more useful in many real-world applications. Traditional approaches usually map multiple views to a unified embedding, in which some weighted mechanisms are utilized to measure the importance of each view. The embedding, serving as a clustering friendly representation, is then sent to extra clustering algorithms. However, a unified embedding cannot cover both complementarity and consistency among views and the weighted scheme measuring the importance of each view as a whole ignores the differences of features in each view. Moreover, because of lacking in proper grouping structure constraint imposed on the unified embedding, it will lead to just multi-view representation learned, which is not clustering friendly. In this paper, we propose a novel multi-view clustering method to alleviate the above problems. By dividing the embedding of a view into unified and view-specific vectors explicitly, complementarity and consistency can be reflected. Besides, an adversarial learning process is developed to force the above embeddings to be non-trivial. Then a fusion strategy is automatically learned, which will adaptively adjust weights for all the features in each view. Finally, a Kullback-Liebler (KL) divergence based objective is developed to constrain the fused embedding for clustering friendly representation learning and to conduct clustering. Extensive experiments have been conducted on various datasets, performing better than the state-of-the-art clustering approaches.