Tensor Singular Value

Tensor singular value decomposition (t-SVD) extends the familiar singular value decomposition to multi-dimensional data (tensors), aiming to efficiently represent and analyze high-dimensional information. Current research focuses on developing computationally efficient t-SVD algorithms, particularly for large-scale tensors, often incorporating techniques like low-rank approximations and factorized gradient descent to reduce computational complexity. These advancements are improving the performance of various applications, including unsupervised feature selection, medical image segmentation (e.g., hippocampus segmentation), and tensor-based neural networks, by enabling more efficient processing and potentially enhancing model generalization.