Tensor ring decomposition employs a powerful technique for understanding high-order tensors. It expresses a tensor as a product of smaller matrices, effectively reducing its complexity. This factorization allows for efficient analysis and offers numerous implementations across various domains. In t

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 latent patterns and structures within the data. This factorization facilitates dimensionality reduction, allowing for effic

Tensor ring decomposition presents a powerful framework for analyzing high-order data. This technique reduces complex tensors into a sum of simpler matrices, often referred to as core matrices and factor matrices. Thus, tensor ring decomposition enables efficient storage and computation with large d

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 natu