Low Rank Tensor

Low-rank tensor methods aim to efficiently represent and analyze high-dimensional data by exploiting underlying low-dimensional structures within multi-way arrays (tensors). Current research focuses on developing efficient algorithms, such as those based on tensor decompositions (e.g., CP, Tucker, TT), for tasks like tensor completion, denoising, and dimensionality reduction, often incorporating techniques like parameter-efficient fine-tuning and rank-adaptive optimization. These advancements are significantly impacting diverse fields, improving the efficiency and accuracy of applications ranging from natural language processing and medical image analysis to hyperspectral imaging and traffic flow prediction.