High-dimensional sparse classification using exponential weighting with empirical hinge loss
Journal article, Peer reviewed
Published version
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https://hdl.handle.net/11250/3132713Utgivelsesdato
2024Metadata
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- Institutt for matematiske fag [2384]
- Publikasjoner fra CRIStin - NTNU [37562]
Sammendrag
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.