Adaptively local consistent concept factorization for multi-view clustering

Many real-world datasets consist of multiple views of data items. The rough method of combining multiple views directly through feature concatenation cannot uncover the optimal latent structure shared by multiple views, which would benefit many data analysis applications. Recently, multi-view clustering methods have emerged and been applied to solving many machine learning problems. However, most multi-view clustering methods ignore the joint information of multi-view data or neglect the quality difference between different views of data, resulting in decreased learning performance. In this paper, we discuss a multi-view clustering algorithm based on concept factorization that effectively fuses useful information to derive a better representation for more effective clustering. We incorporate two regularizers into the concept factorization framework. Specifically, one regularizer is adopted to force the coefficient matrix to move smoothly on the underlying manifold. The other regularizer is used to learn the latent clustering structure from different views. Both of these regularizers are incorporated into the concept factorization framework to learn the latent representation matrix. Optimization problems are solved efficiently via an iterative algorithm. The experimental results on seven real-world datasets demonstrate that our approach outperforms the state-of-the-art multi-view clustering algorithms.