Tensor ring decomposition is a powerful technique for analyzing high-order tensors. It expresses a tensor as a combination of smaller matrices, effectively reducing its complexity. This decomposition allows for efficient analysis and offers numerous implementations across various domains. In the realm of artificial intelligence, tensor ring decomp

Tensor rings offer a efficient approach to representing multidimensional data. By decomposing complex tensors into a sum of rank-1 matrices, tensor ring representations capture underlying patterns and structures within the data. This factorization promotes dimensionality reduction, allowing for sparse storage and processing of high-dimensional info

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

Tensor ring decomposition offers a novel approach to data representation by decomposing high-order tensors into a sum of low-rank matrices. This factorization utilizes the inherent structure within data, enabling efficient storage and processing. Applications range from recommender systems to natural language processing, where tensor decomposition