mlsanityData to 7 Oct 2026

Data Understanding Survey: Pursuing Improved Dataset Characterization Via Tensor-based Methods

arXiv 2510.14161 ↗First posted 15 Oct 2025cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 128 references that are in the map. First posted on arXiv 15 Oct 2025.

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References · 29

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17 more
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  7. 2023Tensor Decomposition for Model Reduction in Neural Networks: A Reviewcited by 14
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