The sparse principal component analysis is a variant of the classical analysis, which finds linear combinations small number features that maximize variance across data. In this paper we propose methodology for adding two general types feature grouping constraints into original PCA optimization procedure.We derive convex relaxations considered constraints, ensuring convexity resulting problem. ...

Principal component regression (PCR) is a simple, but powerful and ubiquitously utilized method. Its effectiveness well established when the covariates exhibit low-rank structure. However, its ability to handle settings with noisy, missing, mixed-valued, that is, discrete continuous, not understood remains an important open challenge. As main contribution of this work, we establish robustness P...