IEEE Trans Neural Netw Learn Syst. 2022 Oct 21;PP. doi: 10.1109/TNNLS.2022.3212390. Online ahead of print.
ABSTRACT
Discrimination problems are of significant interest in the machine learning literature. There has been growing interest in extending traditional vector-based machine learning techniques to their matrix forms. In this article, we investigate the statistical properties of the nuclear-norm-based regularized linear support vector machines (SVMs), in particular establishing the convergence rate of the estimator in the high-dimensional setting. Furthermore, within the distributed estimation paradigm, we propose a communication-efficient estimator that can achieve the same convergence rate. We illustrate the performances of the estimators via some simulation examples and an empirical data analysis.
PMID:36269928 | DOI:10.1109/TNNLS.2022.3212390
