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 … 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 info… 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 decomposi… Read More