Tensor Methods in High Dimensional Data Analysis: Opportunities and Challenges
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Abstract (arXiv)
Large amount of multidimensional data represented by multiway arrays or tensors are prevalent in modern applications across various fields such as chemometrics, genomics, physics, psychology, and signal processing. The structural complexity of such data provides vast new opportunities for modeling and analysis, but efficiently extracting information content from them, both statistically and computationally, presents unique and fundamental challenges. Addressing these challenges requires an interdisciplinary approach that brings together tools and insights from statistics, optimization and numerical linear algebra among other fields. Despite these hurdles, significant progress has been made in the last decade. This review seeks to examine some of the key advancements and identify common threads among them, under eight different statistical settings.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 58 of its 104 references that are in the map. First posted on arXiv 28 May 2024.
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58 of 104 references.
References · 58
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38 more
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