Tensor ring decomposition employs a powerful technique for decomposing high-order tensors. It expresses a tensor as a combination of smaller matrices, effectively simplifying its complexity. This representation allows for efficient analysis and offers numerous applications across various domains. In the realm of machine learning, tensor ring decom… Read More

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 crucial patterns and structures within the data. This factorization promotes dimensionality reduction, allowing for sparse storage and processing of high-dimensional informa… Read More

Tensor ring decomposition presents 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 decompositio… Read More