Open Access

ARTICLE

Multi-Order Neighborhood Fusion Based Multi-View Deep Subspace Clustering

Kai Zhou¹, Yanan Bai², Yongli Hu³, Boyue Wang^3,*

1 Department of Automation, Tsinghua University, Beijing, 100084, China
2 National Center of Technology Innovation for Intelligentization of Politics and Law, Beijing, 100000, China
3 Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Technology, Beijing, 100124, China

* Corresponding Author: Boyue Wang. Email:

Computers, Materials & Continua 2025, 82(3), 3873-3890. https://doi.org/10.32604/cmc.2025.060918

Received 12 November 2024; Accepted 26 December 2024; Issue published 06 March 2025

Abstract

Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data, while the learned representation is difficult to maintain the underlying structure hidden in the origin samples, especially the high-order neighbor relationship between samples. To overcome the above challenges, this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model. We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module. By this design, the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix; then, we can obtain an optimal global affinity matrix where each connected node belongs to one cluster. In addition, the discriminative constraint between views is designed to further improve the clustering performance. A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering methods. The code is available at (accessed on 25 December 2024).

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Keywords

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