In this paper, we investigate the random subsampling method for tensor least squares problem with respect to the popular t-product. From the optimization perspective, we present the error bounds in the sense of probability for the residual and solution obtained by the proposed method. From the statistical perspective, we derive the expressions of the conditional and unconditional expectations and variances for the solution, where the unconditional ones combine the model noises. Moreover, based on the unconditional variance, an optimal subsampling probability distribution is also found. Finally, the feasibility and effectiveness of the proposed method and the correctness of the theoretical results are verified by numerical experiments.
翻译:在本文中,我们调查了与流行的T产品有关的微小最低方位问题的随机子抽样方法。 从优化的角度,我们从剩余和拟议方法获得的解决方案的概率的意义上提出了错误界限。从统计的角度,我们得出了解决方案的有条件和无条件期望和差异的表达方式,无条件的预期和差异将模型噪音结合在一起。此外,根据无条件的差异,还找到了最佳的次抽样概率分布。最后,通过数字实验核实了拟议方法的可行性和有效性以及理论结果的正确性。