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 efficient storage and processing of high-dimensional informa… 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 with large datasets by exploiting the underlying organi… 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 with large datasets by exploiting the underlying patte… 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 large datasets by exploiting the underlying organizat… 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 more info well-defined operations of addition and multiplication, provide a powerful framework… Read More