Work

Class-Driven Robust Non-Negative Matrix Factorization with Dual-Hypergraph Regularization for Data Clustering

Year: 2026

Type: article

Abstract: Traditional non-negative matrix factorization (NMF) faces challenges when dealing with complex data, primarily characterized by sensitivity to noise, neglect of data geometric structure, and inability to effectively utilize supervised information. To address these limitations, this paper proposes a class-driven robust non-negative matrix factorization with dual-hypergraph regularization (CRNMFDH) framework. The core contributions of this framework include the following: Firstly, the design of a novel dual-hypergraph regularization term that symmetrically captures and preserves the higher-order geometric structures of both the sample space and feature space, establishing a mutually reinforcing topological relationship between them. Secondly, an introduction of a class-driven mechanism to effectively integrate label information into the decomposition process, significantly enhancing the discriminative capability of the low-dimensional representations. Finally, the employment of a loss function based on correntropy to replace the traditional Euclidean distance, thereby enhancing the model’s robustness against noise and outliers. Extensive experiments across nine datasets demonstrate that CRNMFDH significantly outperforms existing state-of-the-art algorithms in multiple clustering evaluation metrics and noise robustness, providing an effective new solution for complex data clustering tasks.

Source: Symmetry

DOI: 10.3390/sym18020351

Authors: Haiyan Gao, Gaigai Zhou

Institution: Lanzhou University of Finance and Economics

Language: English

Topic: Face and Expression Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Office for Philosophy and Social Sciences