  With the explosive growth of multi-source data, multi-view clustering has
attracted great attention in recent years. Most existing multi-view methods
operate in raw feature space and heavily depend on the quality of original
feature representation. Moreover, they are often designed for feature data and
ignore the rich topology structure information. Accordingly, in this paper, we
propose a generic framework to cluster both attribute and graph data with
heterogeneous features. It is capable of exploring the interplay between
feature and structure. Specifically, we first adopt graph filtering technique
to eliminate high-frequency noise to achieve a clustering-friendly smooth
representation. To handle the scalability challenge, we develop a novel
sampling strategy to improve the quality of anchors. Extensive experiments on
attribute and graph benchmarks demonstrate the superiority of our approach with
respect to state-of-the-art approaches.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition;Social and Information Networks