Consensus and diversity-fusion partial-view-shared multi-view learning

Abstract

Due to the multi-perspective of data, multi-view learning (MVL) is usually employed. Although existing MVL approaches gain fruitful achievements, they may neglect to learn (a) partial-view-shared knowledge between views; and (b) differences across views. Consequently, inadequate complementary knowledge and weak discriminability may be gained. To address the above problems, Consensus and Diversity-fusion Partial-view-shared Multi-view Learning (CDPMVL) is proposed, which includes two components: (a) Consensus, Partial-view-shared and Specific Component Learning (CPSCL) that partitions the samples into consensual, partial-view-shared, and specific parts, and learns the consensual, partial-view-shared, and specific knowledge of views; and (b) Diversity-fusion Partial-view-shared Knowledge Enhancement (DPKE) that imposes a diversity constraint on partial-view-shared parts and employs a heuristic-based auto-weighting mechanism to highlight the differences among views. By CDPMVL, more complementary relationships between and across views are explored, and the discriminability of the model is enhanced. Extensive experiments performed with eleven algorithms on nine datasets verify the superiority of CDPMVL, which indicates that the incorporation of partial-view-shared knowledge indeed enhances the complementary ability of views. The source code of CDPMVL is available at https://github.com/zzf495/CDPMVL.