10.05.2022 12:45 Marten Wegkamp (Cornell University, Ithaca, New York):
Optimal Discriminant Analysis in High-Dimensional Latent Factor ModelsOnline: attendBC1 2.01.10 (Parkring 11, 85748 Garching)

In high-dimensional classification problems, a commonly used approach is to first project the high-dimensional features into a lower dimensional space, and base the classification on the resulting lower dimensional projections. In this talk, we formulate a latent-variable model with a hidden low-dimensional structure to justify this two-step procedure and to guide which projection to choose. We propose a computationally efficient classifier that takes certain principal components (PCs) of the observed features as projections, with the number of retained PCs selected in a data-driven way. A general theory is established for analyzing such two-step classifiers based on any low-dimensional projections. We derive explicit rates of convergence of the excess risk of the proposed PC-based classifier. The obtained rates are further shown to be optimal up to logarithmic factors in the minimax sense. Our theory allows, but does not require, the lower-dimension to grow with the sample size and the feature dimension exceeds the sample size. Simulations support our theoretical findings. This is joint work with Xin Bing (Department of Statistical Sciences, University of Toronto).