Multi-view Spectral Clustering Based on Topological Manifold Learning

Shaojun Shi, Yibing Liu, Canyu Zhang, Xueling Chen

Abstract: Multi-view clustering is an unsupervised learning strategy that divides data into multiple categories based on complementary and consistent information. Graph-based multi-view clustering methods have attracted much attention due to their simplicity and efficiency. Although graph-based multi-view clustering algorithms have achieved good clustering performance, there are still some issues that need to be addressed. Firstly, existing methods fail to consider the manifold topological structure in the data, which might cause that the constructed similarity graphs are low-quality. Secondly, many graph-based multi-view clustering algorithms treat the construction of similarity graphs and the learning of consistent spectral embedding as two separate procedures, in which the quality of similarity graphs heavily affects the clustering performance. To overcome these problems, we propose a novel method termed as Multi-view Spectral Clustering based on Topological Manifold Learning (MSCTML), where both similarity graph construction and consistent spectral embedding learning are jointly performed in an unified framework. Concretely, affine graph is initially constructed for each view. Subsequently, considering the manifold topological structure in the data, similarity graphs for different views are generated by using the above affine graphs. Furthermore, consistent spectral embedding is learned based on the constructed similarity graphs. Finally, the clustering result is obtained by K-means algorithm. The proposed method is tested on six benchmark datasets. Comparing with single-view and state-of-the-art multi-view clustering algorithms, extensive experimental results demonstrate the superior clustering performance of the proposed MSCTML method.

External IDs:dblp:conf/prcv/ShiLZC24