Incomplete multiview nonnegative representation learning with multiple graphs

作者：Zhang, Nan; Sun, Shiliang^*

来源：Pattern Recognition, 2022, 123: 108412.

DOI：10.1016/j.patcog.2021.108412

• Multiview clustering
• Graph learning
• Incomplete multiview clustering
• Nonnegative matrix factorization
• Multiview clustering

Multiview clustering has become an important research topic during the past decade. However, par-tial 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 frame-work performs matrix factorization on multiple incomplete graphs and decomposes these incomplete graphs into a consensus nonnegative representation and view-specific spectral representations, which in-tegrates the advantages of multiview nonnegative representation learning and graph learning. The pro-posed 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 algo-rithms.