TY - JOUR
T1 - A new graph-based clustering method with dual-feature regularization and Laplacian rank constraint
AU - Zhu, Hengdong
AU - Shen, Yingshan
AU - Zhan, Choujun
AU - Wang, Fu Lee
AU - Weng, Heng
AU - Hao, Tianyong
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2025/1/30
Y1 - 2025/1/30
N2 - The performance of graph-based clustering is commonly limited by two-stage processing (Constructing and dividing similarity graph) and the quality of similar graphs. To this end, we propose a new graph-based clustering method with dual-feature regularization and Laplacian rank constraint. Specifically, our method reveals the clustering structure and unifies the two-stage process. It imposes a Laplacian rank constraint on the similarity graph to ensure that it has C connected components. In addition, a method based on dual-feature regularization is designed to capture local data feature information from both feature extraction and adaptive regression, and is applied to an accurate distance metric learning. A reweighting optimization is integrated to learn a high-quality robust similarity graph. Comprehensive experiments on Ecoli, Yale and Yeast datasets show that our method outperforms the existing graph-based clustering methods with an average improvement of about 4%, 5% and 7% on the evaluation metrics ACC, NMI and RI, respectively.
AB - The performance of graph-based clustering is commonly limited by two-stage processing (Constructing and dividing similarity graph) and the quality of similar graphs. To this end, we propose a new graph-based clustering method with dual-feature regularization and Laplacian rank constraint. Specifically, our method reveals the clustering structure and unifies the two-stage process. It imposes a Laplacian rank constraint on the similarity graph to ensure that it has C connected components. In addition, a method based on dual-feature regularization is designed to capture local data feature information from both feature extraction and adaptive regression, and is applied to an accurate distance metric learning. A reweighting optimization is integrated to learn a high-quality robust similarity graph. Comprehensive experiments on Ecoli, Yale and Yeast datasets show that our method outperforms the existing graph-based clustering methods with an average improvement of about 4%, 5% and 7% on the evaluation metrics ACC, NMI and RI, respectively.
KW - Feature regularization
KW - Graph-based clustering
KW - Laplacian rank constraint
KW - Reweighting
UR - http://www.scopus.com/inward/record.url?scp=85210139650&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2024.112738
DO - 10.1016/j.knosys.2024.112738
M3 - Article
AN - SCOPUS:85210139650
SN - 0950-7051
VL - 309
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
M1 - 112738
ER -