Robust Spectral Embedding Completion Based Incomplete Multi-view Clustering

Abstract

Graph based methods have been widely used in incomplete multi-view clustering (IMVC). Most recent methods try to fill the original missing samples or incomplete affinity matrices to obtain a complete similarity graph for the subsequent spectral clustering. However, recovering the original high-dimensional data or complete n X n similarity matrix is usually time-consuming and noise-sensitive. Besides, they generally separate the cluster indicator learning into an individual step, which may result in sub-optimal graphs or spectral embeddings for clustering. To address these problems, this paper proposes a robust Spectral Embedding Completion based IMVC (SEC-IMVC) method, which incorporates spectral embedding completion and discrete cluster indicator learning into a unified framework. SEC-IMVC performs completion on spectral embeddings, and the embedding noise is eliminated to reduce the negative influence of original data noise. The discrete cluster indicator matrix is seamlessly learned by using spectral rotation, and it can explore the first-order feature consistency among different views. To further improve the completion robustness, the second-order correlation consistency is also captured by pairwise relations alignment. We compare our method with some state-of-the-art approaches on several datasets, and the experimental results show the effectiveness and advantages of our method.