Tensor rings offer a powerful approach to representing multidimensional data. By decomposing complex tensors into a sum of rank-1 matrices, tensor ring representations capture latent patterns and structures within the data. This factorization enables dimensionality reduction, allowing for efficie… Read More

Tensor ring decomposition employs 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. Consequently, tensor ring decomposition enables efficient storage and computation wi… Read More

Tensor ring decomposition presents 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. Therefore, tensor ring decomposition enables efficient storage and computation w… Read More

Tensor ring decomposition presents 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 … Read More

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 get mo… Read More