用张量方法深化数据理解，提升分析可解释性与洞察力。
Data Understanding Survey: Pursuing Improved Dataset Characterization Via Tensor-based Methods
- 引入张量方法替代传统统计与结构分析
- 通过实例揭示数据深层特征，增强可解释性
- 适合需要深度数据洞察的研究者与工程师
在机器学习与数据科学快速发展的背景下，现有的数据集表征方法如统计分析、结构分析和基于模型的分析，往往难以提供创新与可解释性所需的关键洞察。本文综述了当前主流的数据分析技术及其局限性，并探讨了多种张量方法如何为传统数据表征提供更稳健的替代方案。通过具体案例，展示了张量方法如何揭示数据的细微特性，带来更高的可解释性与可操作的智能洞察。文章倡导采用张量化表征，有望显著提升对复杂数据集的理解能力，推动智能、可解释的数据驱动发现。
原文摘要 · Abstract (English)
In the evolving domains of Machine Learning and Data Analytics, existing dataset characterization methods such as statistical, structural, and model-based analyses often fail to deliver the deep understanding and insights essential for innovation and explainability. This work surveys the current state-of-the-art conventional data analytic techniques and examines their limitations, and discusses a variety of tensor-based methods and how these may provide a more robust alternative to traditional statistical, structural, and model-based dataset characterization techniques. Through examples, we illustrate how tensor methods unveil nuanced data characteristics, offering enhanced interpretability and actionable intelligence. We advocate for the adoption of tensor-based characterization, promising a leap forward in understanding complex datasets and paving the way for intelligent, explainable data-driven discoveries.
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