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 factorization allows for efficient analysis and offers numerous utilization across various domains. In

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 latent patterns and structures within the data. This factorization facilitates dimensionality reduction, allowing for sp

Tensor ring decomposition offers a powerful framework for analyzing high-order data. This technique decomposes complex tensors into a sum of simpler matrices, often referred Tensor

Tensor ring decomposition presents a powerful framework for analyzing high-order data. This technique factorizes complex tensors into a sum of simpler matrices, often referred to as core matrices and factor matrices. Consequently, tensor ring decomposition facilitates efficient storage and comput

Tensor ring decomposition employs a powerful framework for analyzing high-order data. This technique decomposes complex tensors into a sum of simpler matrices, often referred to as core matrices and factor matrices. Consequently, tensor ring decomposition enables efficient storage and computation