Data Understanding Survey: Pursuing Improved Dataset Characterization Via Tensor-based Methods
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911213949026304 |
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| author | Merris, Matthew D. Andersen, Tim |
| author_facet | Merris, Matthew D. Andersen, Tim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14161 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data Understanding Survey: Pursuing Improved Dataset Characterization Via Tensor-based Methods Merris, Matthew D. Andersen, Tim Machine Learning A.1; E.0; H.1.0; I.5.0 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. |
| title | Data Understanding Survey: Pursuing Improved Dataset Characterization Via Tensor-based Methods |
| topic | Machine Learning A.1; E.0; H.1.0; I.5.0 |
| url | https://arxiv.org/abs/2510.14161 |