Tensor based dimensionality reduction has recently been extensively studied for computer vision applications. To our knowledge, however, there exist no rigorous error analysis on ...
We consider the task of dimensionality reduction for regression (DRR) whose goal is to find a low dimensional representation of input covariates, while preserving the statistical ...
Model order reduction (MOR) is common in simulation, control and optimization of complex dynamical systems arising in modeling of physical processes and in the spatial discretizati...
7 Linear discriminant analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled p...
Many applications require analyzing vast amounts of textual data, but the size and inherent noise of such data can make processing very challenging. One approach to these issues i...
David G. Underhill, Luke McDowell, David J. Marche...