Randomized Quaternion UTV Decomposition and Randomized Quaternion Tensor UTV Decomposition
<|reference_start|>Randomized Quaternion UTV Decomposition and Randomized Quaternion Tensor UTV Decomposition: In this paper, the quaternion matrix UTV (QUTV) decomposition and quaternion tensor UTV (QTUTV) decomposition are proposed. To begin, the terms QUTV and QTUTV are defined, followed by the algorithms. Subsequently, by employing random sampling from the quaternion normal distribution, randomized QUTV and randomized QTUTV are generated to provide enhanced algorithmic efficiency. These techniques produce decompositions that are straightforward 9 to understand and require minimal cost. Furthermore, theoretical analysis is discussed. Specifically, the upper bounds for approximating QUTV on the rank-K and QTUTV on the TQt-rank K errors are provided, followed by deterministic error bounds and average-case error bounds for the randomized situations, which demonstrate the correlation between the accuracy of the low-rank approximation and the singular values. Finally, numerous numerical experiments are presented to verify that the proposed algorithms work more efficiently and with similar relative errors compared to other comparable decomposition methods. For the novel decompositions, the theory analysis offers a solid theoretical basis and the experiments show significant potential for the associated processing tasks of color images and color videos.<|reference_end|>
@article{yang2024randomized,
    title={Randomized Quaternion UTV Decomposition and Randomized Quaternion Tensor
  UTV Decomposition},
    author={Liqiao Yang, Jifei Miao, Tai-Xiang Jiang, Yanlin Zhang, Kit Ian Kou},
    journal={arXiv preprint arXiv:2406.05734},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05734},
    primaryClass={math.NA cs.NA}
}