Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view
This work addresses scalability and generalization problems in subspace clustering for data analysis applications, representing an incremental improvement over existing methods.
The authors tackled the scalability and generalization issues of graph-based subspace clustering by proposing a framework using anchor points and bipartite graphs, which achieved linear scaling with sample size and outperformed state-of-the-art methods in experiments.
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.