Tensor ring decomposition represents a powerful technique for decomposing high-order tensors. It expresses a tensor as a sum of here smaller matrices, effectively simplifying its complexity. This factorization allows for efficient analysis and offers numerous utilization across various domains. In the realm of machine learning, tensor ring decompo
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 underlying patterns and structures within the data. This factorization facilitates dimensionality reduction, allowing for efficient storage and processing of high-dimensional in
Tensor ring decomposition offers 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 decomposition