Year: 2025
Type: article
Abstract: We propose a novel approach for clustering problem, which refers to as Graph Regularized Orthogonal Subspace Non Negative Matrix Factorization (GNMFOS). This type of model introduces both graph regularization and orthogonality as penalty terms into the objective function. It not only obtains the uniqueness of matrix decomposition but also improves the sparsity of decomposition and reduces computational complexity. Most importantly, using the idea of iteration under weak orthogonality, we construct an auxiliary function for the algorithm and obtain convergence proof to compensate for the lack of convergence proof in similar models. The experimental results show that compared with classical models such as GNMF and NMFOS, our algorithm significantly improves clustering performance and the quality of reconstructed images.
Source: Symmetry
DOI: 10.3390/sym17071154
Authors: Wen Li, Junjian Zhao, Yasong Chen
Institution: Tiangong University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G7172148298
OpenAlex ID: W4412512725