Tensor Recovery Task

Tensor recovery aims to reconstruct incomplete or noisy tensor data by leveraging underlying low-rank structures and smoothness properties. Current research focuses on developing efficient algorithms, such as those based on tensor train (TT) decompositions, Tucker decompositions, and Riemannian gradient descent, to handle high-dimensional data and outliers while providing theoretical guarantees for recovery accuracy. These advancements are improving the performance of various applications, including data clustering, image inpainting, and medical imaging (e.g., susceptibility tensor imaging), where robust and efficient tensor recovery is crucial for accurate analysis and interpretation. The development of hybrid models combining different tensor decomposition methods is also a significant area of ongoing research, aiming to improve both efficiency and accuracy.