A weighted multi-view clustering via sparse graph learning

Abstract

Multi-view clustering considers the diversity of different views and fuses these views to produce a more accurate and robust partition than single-view clustering. It is a key problem of multi-view clustering research to allocate each view reasonably based on its contribution value. In this paper, we propose a weighted multi-view clustering model via sparse graph learning to cope with allocation of different views. The proposed idea is to assign different view weights instead of equal view weights to learn a high-quality shared similarity matrix for multi-view clustering. In our new proposed method, it can consider the clustering capacity heterogeneity of different views in fusion by assigning a weight for each view so that each view special feature are fully excavated, and improve the performance of multi-view clustering. Moreover, our proposed method can directly obtained cluster indicators by imposing low rank constraints without any post-processing operations. In addition, our model is proposed based on sparse graph, so that the outliers and noise in each view data are well handled and the robustness of the algorithm is effectively guaranteed. Finally, numerous experimental results are conducted on different sizes benchmark datasets, and show that the performance of our algorithm is quite satisfactory. The code of our proposed method is publicly available at https://github.com/zhoujie05/A-weighted-multi-view-clustering-via-sparse-graph-learning.