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

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 crucial patterns and structures within the data. This factorization promotes dimensionality reduction, allowing for compa

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 na