A Novel Approach to Learning Consensus and Complementary Information for Multi-View Data Clustering

Abstract

Effective methods are required to be developed that can deal with the multi-faceted nature of the multi-view data. We design a factorization-based loss function-based method to simultaneously learn two components encoding the consensus and complementary information present in multi-view data by using the Coupled Matrix Factorization (CMF) and Non-negative Matrix Factorization (NMF). We propose a novel optimal manifold for multi-view data which is the most consensed manifold embedded in the high-dimensional multi-view data. A new complementary enhancing term is added in the loss function to enhance the complementary information inherent in each view. An extensive experiment with diverse datasets, benchmarking the state-of-the-art multi-view clustering methods, has demonstrated the effectiveness of the proposed method in obtaining accurate clustering solution.