Group information-based dimensionality reduction via canonical correlation analysis

Abstract

As an effective way of avoiding the curse of dimensionality and leveraging the predictive performance in high-dimensional regression analysis, dimension reduction suffers from small sample size. We proposed to utilize group information generated from pairwise data, to learn a low-dimensional representation highly correlated with target value. Experimental results on four public datasets imply that the proposed method can reduce regression error by effective dimension reduction.