Tensor ring decomposition employs a powerful framework for analyzing high-order data. This technique decomposes complex tensors into a sum of simpler matrices, often referred to as core matrices and factor matrices. Consequently, tensor ring decomposition allows efficient storage and computation with large datasets by exploiting the underlying orga… Read More

Tensor ring decomposition offers a powerful framework for analyzing high-order data. This technique decomposes complex tensors into a sum of simpler matrices, often referred to as core matrices and factor matrices. Thus, tensor ring decomposition allows efficient storage and computation with large datasets by exploiting the underlying organization.… Read More

Tensors express multi-dimensional data, extending the concept of vectors and matrices to encompass higher dimensions. These structures find widespread application in fields such as machine learning, physics, and computer graphics. Rings, with their well-defined operations of addition and multiplication, provide a powerful framework for manipulating… Read More

Tensor ring decomposition provides a novel approach to data representation by decomposing high-order tensors into a sum of low-rank matrices. This factorization exploits the inherent structure within data, enabling efficient storage and processing. Applications range from recommender systems to natural language processing, where tensor here decompo… Read More