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

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 facilitates dimensionality reduction, allowing for compact storage and processing of high-dimensional informat

Tensor ring decomposition employs a powerful framework for analyzing high-order data. This technique reduces complex tensors into a sum of simpler matrices, often referred to as core matrices and factor matrices. Therefore, tensor ring decomposition enables efficient storage and computation with large datasets by exploiting the underlying pattern.

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