Centric graph regularized log-norm sparse non-negative matrix factorization for multi-view clustering

Yuzhu Dong, Hangjun Che, Man-Fai Leung, Cheng Liu, Zheng Yan

Abstract: Highlights•The proposed method can learn hidden representations within multi-view data.•The centric graph regularization captures more information to strengthen spatial structure.•The pairwise co-regularization preserves local geometry and inter-view information.•The introduced l2,log<math><msub is="true"><mrow is="true"><mi is="true">l</mi></mrow><mrow is="true"><mn is="true">2</mn><mo is="true">,</mo><mi is="true">l</mi><mi is="true">o</mi><mi is="true">g</mi></mrow></msub></math>-(pseudo) norm obtains better parts-based representation with less redundancy.•Extensive experiments on eight multi-view datasets demonstrate the performance of the proposed method.