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The New England Journal of Statistics in Data Science


Contrastive Inverse Regression for Dimension Reduction
Pub. online: 19 November 2024      Type: Statistical Methodology      Open accessOpen Access
Accepted
3 October 2024
Published
19 November 2024

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 not suitable for analyzing datasets collected from case-control studies. 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. In this paper, we propose a supervised dimension reduction method 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. We prove the convergence of CIR to a local optimum using a gradient descent-based algorithm, and our numerical study empirically demonstrates the improved performance over competing methods for high-dimensional data.

Supplementary material

 Supplementary Material
Additional experimental details are included in the supplementary material.

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© 2024 New England Statistical Society
Open access article under the CC BY license.

Keywords
Case-control studies Supervised dimension reduction Optimization on Stiefel manifold

Funding
SH was supported by NIH grants T32ES007018 and UM1 TR004406; DL was supported by NIH grants R01 AG079291, R56 LM013784, R01 HL149683, and UM1 TR004406, R01 LM014407, P30 ES010126.

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