Currently, high-dimensional data is ubiquitous in data science, which necessitates the development of techniques to decompose and interpret such multidimensional (aka tensor) datasets. Finding a low dimensional representation of the data, that is, its inherent structure, is one of the approaches that can serve to understand the dynamics of low dimensional latent features hidden in the data. Moreover, decomposition methods with non-negative constraints are shown to extract more insightful factors. Nonnegative RESCAL is one such technique, particularly well suited to analyze self-relational data, such as dynamic networks found in international trade flows. Particularly, non-negative RESCAL computes a low dimensional tensor representation by finding the latent space containing multiple modalities. Furthermore, estimating the dimensionality of this latent space is crucial for extracting meaningful latent features. Here, to determine the dimensionality of the latent space with non-ne...

中文翻译：

---

确定国际贸易流中的潜在维度

当前，高维数据在数据科学中无处不在，这需要开发分解和解释此类多维（即张量）数据集的技术。找到数据的低维表示形式，即其固有结构，是可以用来了解隐藏在数据中的低维潜在特征动态的方法之一。此外，具有非负约束的分解方法显示出可以提取更多有见地的因素。非负RESCAL是一种这样的技术，特别适合分析自相关数据，例如在国际贸易流中发现的动态网络。特别地，非负RESCAL通过找到包含多种模态的潜在空间来计算低维张量表示。此外，估计此潜在空间的维数对于提取有意义的潜在特征至关重要。在这里，要确定具有非负数的潜在空间的维数。