A1073
Title: Contrastive inverse regression for dimension reduction
Authors: Didong Li - University of North Carolina at Chapel Hill (United States) [presenting]
Abstract: Supervised dimension reduction (SDR) has been a topic of growing interest in data science, as it enables the reduction of high-dimensional covariates while preserving the functional relation with certain response variables of interest. However, existing SDR methods are unsuitable for analyzing case-control study datasets. In this setting, the goal is to learn and exploit the low-dimensional structure unique to or enriched by the case group, also known as the foreground group. While some unsupervised techniques, such as the contrastive latent variable model and its variants, have been developed for this purpose, they fail to preserve the functional relationship between the dimension-reduced covariates and the response variable. A supervised dimension reduction method is proposed, called contrastive inverse regression (CIR), specifically designed for the contrastive setting. CIR introduces an optimization problem defined on the Stiefel manifold with a non-standard loss function. The convergence of CIR to a local optimum using a gradient descent-based algorithm and the numerical study empirically demonstrates the improved performance over competing methods for high-dimensional biomedical data are proved.
