TY  - EJOU
AU  - Zhou, Kai 
AU  - Bai, Yanan 
AU  - Hu, Yongli 
AU  - Wang, Boyue 

TI  - Multi-Order Neighborhood Fusion Based Multi-View Deep Subspace Clustering
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 82
IS  - 3
SN  - 1546-2226

AB  - 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).
KW  - Multi-view subspace clustering; subspace clustering; deep clustering; multi-order graph structure

DO  - 10.32604/cmc.2025.060918