Factor Tensor

Factor tensors represent multi-dimensional data as a product of lower-dimensional components, aiming to extract underlying latent structures and improve computational efficiency. Current research focuses on developing efficient algorithms for tensor decomposition, including factorized gradient descent and methods leveraging deep neural networks like tensor transformers and convolutional neural networks, often incorporating techniques to mitigate heterogeneity among factor tensors. These advancements are impacting various fields, enhancing prediction accuracy and reducing computational costs in applications ranging from temporal knowledge graph embedding to robust principal component analysis and multi-task learning. The improved efficiency and robustness of these methods are driving significant progress in handling large-scale, complex datasets.