Dynamic-Fusion Multi-view Projection Clustering Algorithm

Abstract

Multi-view clustering is a hot research area, which has attracted increasing attention. Most existing multi- view clustering methods usually learn the data first, and then cluster the fused unified graph to get the final result. This two-step strategy of graph learning and graph clustering may lead to the randomness of clustering results. Besides, the inevitable noise of the data itself and the large differences among views, these invalid fusion methods in high-dimensional data space may cause important information loss, and different multi-view data may be sensitive to parameter selections. To solve the above problems, a multi-view projection clustering algorithm based on dynamic fusion is proposed, which integrates adaptive dimensionality reduction graph learning, self-weight fusion without parameters and spectral clustering in the same framework. The three processes promote each other and jointly optimize the projection matrix, similarity matrix, consensus matrix and clustering label. The Laplacian matrix of the best consensus matrix obtained by dynamic fusion is constrained by rank, and clustering results are obtained directly. Moreover, heuristic super-parameters are automatically adjusted with each optimization iteration. To solve the joint optimization problem, an effective alternative optimization method is designed. Experimental results on artificial datasets and real datasets show the superiority of the algorithm.

Keywords

• multi-view clustering; projection dimension reduction; graph fusion; consensus matrix