Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview

By Zhao Kang, Zhiping Lin, Xiaofeng Zhu, Wenbo Xu. IEEE Transactions on Cybernetics, 2021

323 citations recorded by the source corpus.

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 <inline-formula> <tex-math notation="LaTeX">$n\times n$ </tex-math></inline-formula> graph, where <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-means clustering. Moreover, a model to process multiview data is also proposed, which is linearly scaled with respect to <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. Extensive experiments demonstrate the efficiency and effectiveness of our approach with respect to many state-of-the-art clustering methods.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/99cc4333f65c50bbbe4caa84e9ebbdebadc3fd35

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chuan Zhou
2. Philip S. Yu
3. Feng Xia
4. Shirui Pan
5. Yixin Liu
6. Ming Jin
7. Charu C. Aggarwal
8. Bing Liu

In Statistical and Nonlinear Physics

1. Philip S. Yu
2. Chuan Zhou
3. Yixin Liu
4. Feng Xia
5. Shirui Pan

Citations in the corpus

• Multi-view Contrastive Graph Clustering (2021, unknown): “The real-life data are often collected from various sources or obtained from different extractors, thus are naturally represented by different features or views [Kang et al., 2021, 2020c].”
• Graph Self-Supervised Learning: A Survey (2021, background): “To achieve that, a mapping function V : V ! C is introduced to acquire the pseudo label for each node, which is built on specific unsupervised clustering/partitioning algorithms [59], [60], [61], [62].”