Tensor rings offer a powerful 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 enables dimensionality reduction, allowing for sparse storage and processing of high-dimensional informatio… Read More

Tensor ring decomposition presents 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 decompositio… Read More