Tensor ring decomposition is a powerful technique for decomposing high-order tensors. It expresses a tensor as a combination of smaller matrices, effectively representing its complexity. This representation allows for efficient processing and offers numerous applications across various domains. In the realm of machine learning, tensor ring decompo

Tensor rings offer a powerful 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 infor

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. Thus, tensor ring decomposition enables efficient storage and computation with large datasets by exploiting the underlying structure. Appl

Tensor ring decomposition presents a powerful framework for analyzing high-order data. This technique decomposes complex tensors into a sum of simpler matrices, often referred check here to as core matrices and factor matrices. Consequently, tensor ring decomposition facilitates efficient storage and computation with large datasets by exploiting th

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