As the size and complexity of data continue to increase, the need for efficient and effective analysis methods becomes ever more crucial. Tensorization, the process of converting 2-dimensional datasets into multidimensional structures, has emerged as a promising approach for multiway analysis methods. This paper explores the steps involved in tensorization, multidimensional data sources, various multiway analysis methods employed, and the benefits of these approaches. A small example of Blind Source Separation (BSS) is presented comparing 2-dimensional algorithms and a multiway algorithm in Python. Results indicate that multiway analysis is more expressive. Additionally, tensorization techniques aid in compressing deep learning models by reducing the number of required parameters while enhancing the expression of relationships across dimensions. A survey of the multi-away analysis methods and integration with various Deep Neural Networks models is presented using case studies in different domains.

翻译：随着数据规模和复杂性的持续增长，对高效且有效的分析方法的需求变得愈发关键。张量化——将二维数据集转换为多维结构的过程——已成为多路分析方法的一种有前景的途径。本文探讨了张量化的步骤、多维数据源、采用的各种多路分析方法，以及这些方法的优势。文中通过一个盲源分离（BSS）的小型示例，在Python中比较了二维算法与多路算法的性能。结果表明，多路分析具有更强的表达能力。此外，张量化技术通过减少所需参数数量同时增强跨维度关系的表达，有助于压缩深度学习模型。本文还结合不同领域的案例研究，对多路分析方法及其与各种深度神经网络的集成进行了综述。