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 of machine learning, tensor ring decomposition en

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 storage and processing of high-dimensional information.

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