Regularized Projection Matrix Approximation with Applications to Community Detection
<|reference_start|>Regularized Projection Matrix Approximation with Applications to Community Detection: This paper introduces a regularized projection matrix approximation framework aimed at recovering cluster information from the affinity matrix. The model is formulated as a projection approximation problem incorporating an entrywise penalty function. We explore three distinct penalty functions addressing bounded, positive, and sparse scenarios, respectively, and derive the Alternating Direction Method of Multipliers (ADMM) algorithm to solve the problem. Then, we provide a theoretical analysis establishing the convergence properties of the proposed algorithm. Extensive numerical experiments on both synthetic and real-world datasets demonstrate that our regularized projection matrix approximation approach significantly outperforms state-of-the-art methods in terms of clustering performance.<|reference_end|>
@article{zhai2024regularized,
    title={Regularized Projection Matrix Approximation with Applications to
  Community Detection},
    author={Zheng Zhai, Jialu Xu, Mingxin Wu, and Xiaohui Li},
    journal={arXiv preprint arXiv:2405.16598},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16598},
    primaryClass={cs.LG}
}