Fine-Grained Bipartite Concept Factorization for Clustering
Abstract
In this paper we propose a novel concept factorization method that seeks factor matrices using a cross-order positive semi-definite neighbor graph which provides comprehensive and complementary neighbor information of the data. The factor matrices are learned with bipartite graph partitioning which exploits explicit cluster structure of the data and is more geared towards clustering application. We develop an effective and efficient optimization algorithm for our method and provide elegant theoretical results about the convergence. Extensive experimental results confirm the effectiveness of the proposed method.
Cite
Text
Peng et al. "Fine-Grained Bipartite Concept Factorization for Clustering." Conference on Computer Vision and Pattern Recognition, 2024. doi:10.1109/CVPR52733.2024.02481Markdown
[Peng et al. "Fine-Grained Bipartite Concept Factorization for Clustering." Conference on Computer Vision and Pattern Recognition, 2024.](https://mlanthology.org/cvpr/2024/peng2024cvpr-finegrained/) doi:10.1109/CVPR52733.2024.02481BibTeX
@inproceedings{peng2024cvpr-finegrained,
title = {{Fine-Grained Bipartite Concept Factorization for Clustering}},
author = {Peng, Chong and Zhang, Pengfei and Chen, Yongyong and Kang, Zhao and Chen, Chenglizhao and Cheng, Qiang},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2024},
pages = {26264-26274},
doi = {10.1109/CVPR52733.2024.02481},
url = {https://mlanthology.org/cvpr/2024/peng2024cvpr-finegrained/}
}