Tensor Model

Tensor models are mathematical frameworks used to represent and analyze multi-dimensional data, aiming to extract meaningful patterns and insights from complex datasets. Current research focuses on developing efficient algorithms for tensor decomposition and factorization, including Bayesian and non-negative approaches, as well as incorporating domain knowledge through graph regularization or adapting models to handle heteroskedastic noise and continuous indices. These advancements are improving the accuracy and efficiency of tensor-based methods in diverse applications such as dynamic network analysis, large language model training, and image processing, ultimately leading to more robust and interpretable data analysis.