Tensor rings offer a novel 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 compact storage and processing of high-dimensional informat

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