Collection
Tensor Methods
Research in tensor (multidimensional array) analysis is accelerating, driven by the increasing complexity of data sets, arising from fields such as biomedical engineering, networks, and the physical and social sciences, which naturally lend themselves to a tensor structure. Tensor factorization methods in particular have been shown to aid in low-dimensional embedding and clustering, data compression, denoising, and imputation. Thus, the field has seen a substantial growth in factorization strategies in recent years, leading to challenges in deployment of these methods to real-world data. These challenges include questions of existence and uniqueness of optimal solutions, rank and model selection, and computational complexity.
The purpose of this Topical Collection is to invite research, survey, and review articles on novel theoretical, computational, and real-world application progress in tensor analysis. Our goal is to bring together new developments in tensor analysis into a Topical Collection at La Matematica in order to give interested readers a broad and up-to-date opportunity to learn about new work in tensor analysis, thereby lowering the barrier for entry into the field, and to feature wide array of research and researchers in this field.
Editors
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Dr. Jamie Haddock
Harvey Mudd College (USA)
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Dr. Anna Konstorum
Center for Computing Sciences, Institute for Defense Analyses (USA) and University of Florida (USA)
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Dr. Anna Ma
UC Irvine (USA)
