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

• URL: http://arxiv.org/abs/2510.14161v1
• Date: Wed, 15 Oct 2025 23:17:54 GMT
• Title: Data Understanding Survey: Pursuing Improved Dataset Characterization Via Tensor-based Methods
• Authors: Matthew D. Merris, Tim Andersen,
• Abstract summary: Existing dataset characterization methods often fail to deliver the deep understanding and insights essential for innovation and explainability.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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