Incomplete Multi-view Clustering via Subspace Learning

Abstract

Multi-view clustering, which explores complementary information between multiple distinct feature sets for better clustering, has a wide range of applications, e.g., knowledge management and information retrieval. Traditional multi-view clustering methods usually assume that all examples have complete feature sets. However, in real applications, it is often the case that some examples lose some feature sets, which results in incomplete multi-view data and notable performance degeneration. In this paper, a novel incomplete multi-view clustering method is therefore developed, which learns unified latent representations and projection matrices for the incomplete multi-view data. To approximate the high level scaled indicator matrix defined to represent class label matrix, the latent representations are expected to be non-negative and column orthogonal. Besides, since data are often with high dimensional and noisy features, the projection matrices are enforced to be sparse so as to select relevant features when learning the latent space. Furthermore, the inter-view and intra-view data structure is preserved to further enhance the clustering performance. To these ends, an objective is developed with efficient optimization strategy and convergence analysis. Extensive experiments demonstrate that our model performs better than the state-of-the-art multi-view clustering methods in various settings.