Non-Negative Reduced Biquaternion Matrix Factorization with Applications in Color Face Recognition
<|reference_start|>Non-Negative Reduced Biquaternion Matrix Factorization with Applications in Color Face Recognition: Reduced biquaternion (RB), as a four-dimensional algebra highly suitable for representing color pixels, has recently garnered significant attention from numerous scholars. In this paper, for color image processing problems, we introduce a concept of the non-negative RB matrix and then use the multiplication properties of RB to propose a non-negative RB matrix factorization (NRBMF) model. The NRBMF model is introduced to address the challenge of reasonably establishing a non-negative quaternion matrix factorization model, which is primarily hindered by the multiplication properties of traditional quaternions. Furthermore, this paper transforms the problem of solving the NRBMF model into an RB alternating non-negative least squares (RB-ANNLS) problem. Then, by introducing a method to compute the gradient of the real function with RB matrix variables, we solve the RB-ANNLS optimization problem using the RB projected gradient algorithm and conduct a convergence analysis of the algorithm. Finally, we validate the effectiveness and superiority of the proposed NRBMF model in color face recognition.<|reference_end|>
@article{miao2024non-negative,
    title={Non-Negative Reduced Biquaternion Matrix Factorization with Applications
  in Color Face Recognition},
    author={Jifei Miao, Junjun Pan, and Michael K. Ng},
    journal={arXiv preprint arXiv:2408.05582},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05582},
    primaryClass={cs.CV cs.NA math.NA}
}