Multi-view Clustering with Latent Low-rank Proxy Graph Learning

With advances in information acquisition technologies, multi-view data are increasing dramatically in a variety of real-world applications, whereas such data is usually corrupted by noises and outliers. Many existing multi-view graph clustering (MVGC) methods usually learn a consensus affinity graph using a late-fusion scheme in semantic space, which compound the challenge of leveraging the underlying relationships among corrupted multi-view data. In this paper, we propose a novel clustering method for handing corrupted multi-view data, hereafter referred to as Latent Low-Rank Proxy Graph Learning (LLPGL). Specifically, by projecting the multi-view data into a low-dimension proxy feature space, LLPGL can learn a low-dimension yet low-rank latent proxy from corrupted view data. Meanwhile, by employing the adaptive neighbor graph learning over the clean proxy, a high-quality affinity graph can be learned for clustering purpose. Then, an effective optimization algorithm is proposed to solve the model of LLPGL. Experimental results on five widely used real-world benchmarks validate the effectiveness of the proposed method.Consequently, the proposed method can be used to cluster the corrupted multi-view data for real-life applications.