Tensor decomposition is now being used for data analysis, information compression, and knowledge recovery. However, the mathematical property of tensor decomposition is not yet fully clarified because it is one of singular learning machines. In this paper, we give the upper bound of its real log canonical threshold (RLCT) of the tensor decomposition by using an algebraic geometrical method and derive its Bayesian generalization error theoretically. We also give considerations about its mathematical property through numerical experiments.

翻译：电离分解目前正在用于数据分析、信息压缩和知识恢复。然而,电离分解的数学属性尚未完全明确,因为它是单数学习机器之一。在本文中,我们使用代数几何几何法,给其真对数分解的圆柱形阈值(RLCT)上层圈,并从理论上得出其贝叶斯概括错误。我们还通过数字实验来考虑其数学属性。</s>