Incomplete Multiview Nonnegative Representation Learning with Multiple Graphs

Abstract Multiview clustering has become an important research topic during the past decade. However, partial views of many data instances are missing in some realistic multiview learning scenarios. To handle this problem, we develop an effective incomplete multiview nonnegative representation learning (IMNRL) framework, which is suitable for incomplete multiview clustering in various situations. The IMNRL framework performs matrix factorization on multiple incomplete graphs and decomposes these incomplete graphs into a consensus nonnegative representation and view-specific spectral representations, which integrates the advantages of multiview nonnegative representation learning and graph learning. The proposed framework has the following merits: (1) it learns a consensus nonnegative embedding and view-specific embeddings simultaneously; (2) the nonnegative embedding satisfies the neighbor constraint on each incomplete view, which directly reveals the multiview clustering results. Experimental results show that the proposed framework outperforms other state-of-the-art incomplete multiview clustering algorithms.