Tensor rings offer a efficient approach to representing multidimensional data. By decomposing complex tensors into a sum of rank-1 matrices, tensor ring representations capture crucial patterns and structures within the data. This factorization facilitates dimensionality reduction, allowing for efficient storage and processing of high-dimensional i… Read More

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

Tensor ring decomposition offers 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 decomposition … Read More