Tensor Ring Decomposition

Tensor ring decomposition is a powerful technique for representing and manipulating high-dimensional data by expressing tensors as a network of smaller, interconnected core tensors. Current research focuses on improving the efficiency and robustness of tensor ring decomposition algorithms, particularly for large-scale datasets with missing or noisy entries, and applying it to various machine learning tasks such as neural network compression, density estimation, and image processing. This approach offers significant advantages in terms of reduced storage and computational costs, leading to improved performance and scalability in diverse applications, including hyperspectral image fusion and dynamic network analysis.