Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view

• URL: http://arxiv.org/abs/2102.07943v1
• Date: Tue, 16 Feb 2021 03:46:11 GMT
• Title: Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view
• Authors: Zhao Kang, Zhiping Lin, Xiaofeng Zhu, Wenbo Xu
• Abstract summary: Graph-based subspace clustering methods have exhibited promising performance. They still suffer some of these drawbacks: encounter the expensive time overhead, fail in exploring the explicit clusters, and cannot generalize to unseen data points. We propose a scalable graph learning framework, seeking to address the above three challenges simultaneously.
• Score: 28.779909990410978
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: encounter the expensive time overhead, fail in exploring 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 a $n\times n$ 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 multi-view data is also proposed, which is linear 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.

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