Multi-view Spectral Clustering on the Grassmannian Manifold With Hypergraph Representation

• URL: http://arxiv.org/abs/2503.06066v1
• Date: Sat, 08 Mar 2025 05:26:53 GMT
• Title: Multi-view Spectral Clustering on the Grassmannian Manifold With Hypergraph Representation
• Authors: Murong Yang, Shihui Ying, Xin-Jian Xu, Yue Gao,
• Abstract summary: We introduce a novel approach to generate hypergraphs by leveraging sparse representation learning from data points.<n>We propose an optimization function with orthogonality constraints for multi-view hypergraph spectral clustering, which incorporates spectral clustering for each view and ensures consistency across different views.<n>To validate the effectiveness of the proposed algorithm, we test it on four real-world multi-view datasets and compare its performance with seven state-of-the-art multi-view clustering algorithms.
• Score: 13.154074955545406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph-based multi-view spectral clustering methods have achieved notable progress recently, yet they often fall short in either oversimplifying pairwise relationships or struggling with inefficient spectral decompositions in high-dimensional Euclidean spaces. In this paper, we introduce a novel approach that begins to generate hypergraphs by leveraging sparse representation learning from data points. Based on the generated hypergraph, we propose an optimization function with orthogonality constraints for multi-view hypergraph spectral clustering, which incorporates spectral clustering for each view and ensures consistency across different views. In Euclidean space, solving the orthogonality-constrained optimization problem may yield local maxima and approximation errors. Innovately, we transform this problem into an unconstrained form on the Grassmannian manifold. Finally, we devise an alternating iterative Riemannian optimization algorithm to solve the problem. To validate the effectiveness of the proposed algorithm, we test it on four real-world multi-view datasets and compare its performance with seven state-of-the-art multi-view clustering algorithms. The experimental results demonstrate that our method outperforms the baselines in terms of clustering performance due to its superior low-dimensional and resilient feature representation.

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