Supervised Dimensionality Reduction Via Nonlinear Target Estimation

Dimensionality reduction is a crucial ingredient of machine learning and data mining, boosting classification accuracy through the isolation of patterns via omission of noise. Nevertheless, recent studies have shown that dimensionality reduction can benefit from label information, via a joint estimation of predictors and target variables from a low-rank representation. In the light of such inspiration, we propose a novel dimensionality reduction which simultaneously reconstructs the predictors using matrix factorization and estimates the target variable via a dual-form maximum margin classifier from the latent space. The joint optimization function is learned through a coordinate descent algorithm via stochastic updates. Finally empirical results demonstrate the superiority of the proposed method compared to both classification in the original space (no reduction), or classification after unsupervised reduction.