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

Tensor rings provide a novel and powerful framework for optimal representation learning. By decomposing high-order tensors into a sum of lower-rank tensors, tensor ring models represent complex data structures in a more compact manner. This reduction of dimensionality leads to significant benefit