Demystifying Tubal Tensor Algebra
Haim Avron, Uria Mor

TL;DR
This paper clarifies the foundational principles of tubal tensor algebra, demonstrating that the t-product and its generalizations naturally arise from algebraic properties and are uniquely defined by them, thus strengthening the theoretical basis of the framework.
Contribution
It provides a cohesive, accessible foundation for tubal tensor algebra, proves the uniqueness of the $ ext{star}_M$-product, and addresses theoretical gaps in the framework.
Findings
The t-product arises naturally from algebraic properties.
The $ ext{star}_M$-product is the unique product satisfying certain properties.
The paper enhances the theoretical understanding of tubal tensor algebra.
Abstract
Developed in a series of seminal papers in the early 2010s, the tubal tensor framework provides a clean and effective algebraic setting for tensor computations, supporting matrix-mimetic features such as a tensor Singular Value Decomposition and Eckart-Young-like optimality results. It has proven to be a powerful tool for analyzing inherently multilinear data arising in hyperspectral imaging, medical imaging, neural dynamics, scientific simulations, and more. At the heart of tubal tensor algebra lies a special tensor-tensor product: originally the t-product, later generalized into a full family of products via the -product. Though initially defined through the multiplication of a block-circulant unfolding of one tensor by a matricization of another, it was soon observed that the t-product can be interpreted as standard matrix multiplication where the scalars are tubes-i.e.,…
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Taxonomy
TopicsTensor decomposition and applications · Neural Networks and Reservoir Computing · Model Reduction and Neural Networks
