Abstract: Aiming at the shortcomings of incomplete multi-view clustering, we  proposed a unified framework that integrated self-representation and projection mapping. Firstly, self-representation and sample presence indication matrices were used to learn a uniform similarity graph, which reflected the common similarity relationship between samples. Secondly, the sample matrices were projected onto the hypersphere by using projection mapping to obtain a common low-dimensional representation. Finally, the two were embedded together through spectral representation to solve the incomplete multi-view clustering problem caused by missing multi-view data. The experimental results of this algorithm on real datasets are better than other algorithms, which proves the effectiveness of the proposed algorithm.

Key words: multi-view clustering, incomplete view, self-representation learning, projection mapping