High-dimensional sparse classification using exponential weighting with empirical hinge loss

The Tien Mai

doi:10.1111/stan.12342

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arXiv · 2312.12952

High-dimensional sparse classification using exponential weighting with empirical hinge loss

The Tien Mai

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Abstract

In this study, we address the problem of high-dimensional binary classification. Our proposed solution involves employing an aggregation technique founded on exponential weights and empirical hinge loss. Through the employment of a suitable sparsity-inducing prior distribution, we demonstrate that our method yields favorable theoretical results on prediction error. The efficiency of our procedure is achieved through the utilization of Langevin Monte Carlo, a gradient-based sampling approach. To illustrate the effectiveness of our approach, we conduct comparisons with the logistic Lasso on simulated data and a real dataset. Our method frequently demonstrates superior performance compared to the logistic Lasso.

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The Tien Mai. 2023-12-20. High-dimensional sparse classification using exponential weighting with empirical hinge loss. https://doi.org/10.1111/stan.12342

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