Learnable Graph Filter for Multi-view Clustering

Peng Zhou, Liang Du

Abstract: Multi-view clustering is an important machine learning task for multi-media data. Recently, graph filter based multi-view clustering achieves promising performance and attracts much attention. However, the conventional graph filter based methods only use a pre-defined graph filter for each view and the used graph filters ignore the rich information among all views. Different from the conventional methods, in this paper, we aim to tackle a new problem, i.e., instead of using the pre-defined graph filters, how to construct an appropriate consensus graph filter by considering the information in all views. To achieve this, we propose a novel multi-view clustering method with graph filter learning. In our method, we learn an appropriate consensus graph filter from all views of data with multiple graph learning rather than directly pre-defining it. Then, we provide an iterative algorithm to obtain the consensus graph filter and analyze why it can lead to better clustering results. The extensive experiments on benchmark data sets demonstrate the effectiveness and superiority of the proposed method. The codes of this article are released in http://Doctor-Nobody.github.io/codes/MCLGF.zip.