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 enables dimensionality reduction, allowing for efficient storage and processing of high-dimensional in

Tensor ring decomposition offers 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 facilitates efficient storage and computation with large datasets by exploiting the underlying organi

Tensor ring decomposition employs 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. Thus, tensor ring decomposition allows efficient storage and computation with large datasets by exploiting the underlying structure. A

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

Tensors represent multi-dimensional data, extending the concept of vectors and matrices to encompass higher dimensions. These structures find widespread application in fields such as machine learning, physics, and computer graphics. Rings, with their well-defined operations of addition and multiplication, provide a powerful framework for manipulati