Tensor ring decomposition offers a powerful framework for analyzing high-order data. This technique decomposes 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 wit

Tensors articulate multi-dimensional data, extending the concept of vectors and matrices to encompass higher dimensions. These structures find widespread application in fields such as machine learning, physics, and computer graphics. Rings, with their well-defined operations of addition and multipli

Tensor ring decomposition offers a novel approach to data representation by decomposing high-order tensors into a sum of low-rank matrices. This factorization leverages the inherent structure within data, enabling efficient storage and processing. Applications range from recommender systems to natur