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 deco… 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 decomposi… Read More