Embedded Feature Selection on Graph-Based Multi-View Clustering
DOI:
https://doi.org/10.1609/aaai.v38i15.29645Keywords:
ML: Multi-instance/Multi-view Learning, ML: Clustering, ML: Multi-class/Multi-label Learning & Extreme Classification, ML: Multimodal LearningAbstract
Recently, anchor graph-based multi-view clustering has been proven to be highly efficient for large-scale data processing. However, most existing anchor graph-based clustering methods necessitate post-processing to obtain clustering labels and are unable to effectively utilize the information within anchor graphs. To solve these problems, we propose an Embedded Feature Selection on Graph-Based Multi-View Clustering (EFSGMC) approach to improve the clustering performance. Our method decomposes anchor graphs, taking advantage of memory efficiency, to obtain clustering labels in a single step without the need for post-processing. Furthermore, we introduce the l2,p-norm for graph-based feature selection, which selects the most relevant data for efficient graph factorization. Lastly, we employ the tensor Schatten p-norm as a tensor rank approximation function to capture the complementary information between different views, ensuring similarity between cluster assignment matrices. Experimental results on five real-world datasets demonstrate that our proposed method outperforms state-of-the-art approaches.Downloads
Published
2024-03-24
How to Cite
Zhao, W., Li, G., Yang, H., Gao, Q., & Wang, Q. (2024). Embedded Feature Selection on Graph-Based Multi-View Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 38(15), 17016-17023. https://doi.org/10.1609/aaai.v38i15.29645
Issue
Section
AAAI Technical Track on Machine Learning VI