Incomplete multiview nonnegative representation learning with multiple graphs

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...