Tensor ring decomposition represents a powerful technique for understanding high-order tensors. It expresses a tensor as a sum of smaller matrices, effectively reducing its complexity. This decomposition allows for efficient computation and offers numerous utilization across various domains. In the realm of data science, tensor ring decomposition … Read More

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