Self-weighted Multi-view Subspace Clustering With Low Rank Tensor Constraint

Abstract

Manufacturing process is making a difference in the Product-Service Systems (PSS). In the manufacturing process, the real-time high-dimensional data will be generated by the production line. In the field of high-dimensional data analysis, a very important analysis method is high-dimensional data clustering, and subspace clustering is one of the effective methods of high-dimensional data clustering. Clustering is an important topic in data mining. In real world, the data represented with multi view information makes the clustering algorithms more difficult. In this paper, we explore an improved multi-view clustering algorithm. Unlike the conventional multi-view subspace clustering methods that usually average the weight of views, the proposed algorithm simultaneously takes into account the expression and weight of each view. Specifically, every view is re-represented with the complementary information from multiple views by low rank tensor, and the views are fused by exploring a Laplacian rank constrained graph. With the complementary information from multiple views and the different weights of the views, the proposed algorithm is improved. Experimental results on various real-world datasets demonstrate the effectiveness of our research.