Low Rank Tensor Model

Low-rank tensor models represent high-dimensional data using a compact, lower-dimensional structure, aiming to improve efficiency and accuracy in various applications. Current research focuses on developing and applying these models in diverse fields, including machine learning (e.g., parameter-efficient fine-tuning of large language models), generalized linear models, and time-series analysis, often employing algorithms like block coordinate descent or alternating direction method of multipliers. The resulting computational efficiency and improved parameter estimation, particularly with limited data, make low-rank tensor models valuable tools for analyzing complex datasets in diverse scientific domains and practical applications like medical imaging.