Online Tensor

Online tensor methods focus on efficiently processing and analyzing sequentially arriving multi-dimensional data, addressing limitations of traditional offline approaches that struggle with the large size and complexity of tensor data. Current research emphasizes developing online algorithms, such as stochastic gradient descent and Riemannian gradient descent, often coupled with low-rank tensor decompositions like PARAFAC, to achieve real-time inference and prediction. These advancements are impacting diverse fields, including targeted advertising, reinforcement learning, and structural health monitoring, by enabling efficient analysis of high-dimensional, time-evolving data streams.