Tensor Recovery

Tensor recovery focuses on reconstructing incomplete or noisy multi-dimensional data (tensors) by leveraging underlying low-rank structures or sparsity patterns. Current research emphasizes efficient algorithms, such as those based on tensor decompositions (e.g., Tucker, Tensor Train, Tensor Ring), Kaczmarz methods, and factorized gradient descent, often incorporating regularization techniques (e.g., ℓ1-norm, tensor nuclear norm) to improve robustness and accuracy. These advancements are crucial for handling large-scale datasets in various applications, including image and video processing, hyperspectral imaging, and medical imaging, where efficient and accurate tensor recovery is essential for data analysis and interpretation.