Order Tensor

Order tensors are multi-dimensional arrays used to represent complex, high-dimensional data, with research focusing on efficient decomposition and manipulation techniques for improved analysis and application. Current efforts concentrate on developing faster algorithms for low-rank tensor approximations (e.g., using CP, Tucker, and Tensor Train decompositions), robust tensor completion methods handling noise and missing data, and efficient computation of multilinear operations within neural networks. These advancements are crucial for handling the ever-increasing volume and complexity of data in various fields, including machine learning, computer vision, and signal processing, enabling more efficient and accurate analysis of multi-modal datasets.