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

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 enables dimensionality reduction, allowing for sparse storag

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 natur