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

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

Tensor ring decomposition offers a powerful framework for analyzing high-order data. This technique factorizes complex tensors into a sum of simpler matrices, often referred to as core matrices and factor matrices. Consequently, tensor ring decomposition enables efficient storage and computation with large datasets by exploiting the underlying p… Read More

Tensor ring decomposition offers a powerful framework for analyzing high-order data. This technique reduces complex tensors into a sum of simpler matrices, often referred to as core matrices and factor matrices. Thus, tensor ring decomposition facilitates efficient storage and computation with large datasets by exploiting the underlying organiza… Read More

Tensor ring decomposition offers 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 decompositi… Read More