@Article{cmc.2025.060918,
AUTHOR = {Kai Zhou, Yanan Bai, Yongli Hu, Boyue Wang},
TITLE = {Multi-Order Neighborhood Fusion Based Multi-View Deep Subspace Clustering},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {82},
YEAR = {2025},
NUMBER = {3},
PAGES = {3873--3890},
URL = {http://www.techscience.com/cmc/v82n3/59931},
ISSN = {1546-2226},
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).},
DOI = {10.32604/cmc.2025.060918}
}