Research Area:  Machine Learning

Abstract:

Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: they encounter the expensive time overhead, they fail to explore the explicit clusters, and cannot generalize to unseen data points. In this work, we propose a scalable graph learning framework, seeking to address the above three challenges simultaneously. Specifically, it is based on the ideas of anchor points and bipartite graph. Rather than building an n×n graph, where n is the number of samples, we construct a bipartite graph to depict the relationship between samples and anchor points. Meanwhile, a connectivity constraint is employed to ensure that the connected components indicate clusters directly. We further establish the connection between our method and the K -means clustering. Moreover, a model to process multiview data is also proposed, which is linearly scaled with respect to n . Extensive experiments demonstrate the efficiency and effectiveness of our approach with respect to many state-of-the-art clustering methods.

Keywords:  
Bipartite graph
Clustering methods
Periodic structures
Laplace equations
Clustering algorithms
Videos
Training

Author(s) Name:  Zhao Kang; Zhiping Lin; Xiaofeng Zhu

Publisher name:  IEEE

DOI:  10.1109/TCYB.2021.3061660

Volume Information:  Volume: 52

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9380779