FEATURE ELIMINATION IN KERNEL MACHINES IN MODERATELY HIGH DIMENSIONS.

IF 3.2 1区数学 Q1 STATISTICS & PROBABILITY

Annals of Statistics Pub Date : 2019-02-01 DOI:10.1214/18-AOS1696

Sayan Dasgupta, Yair Goldberg, Michael R Kosorok

引用次数: 18

Abstract

We develop an approach for feature elimination in statistical learning with kernel machines, based on recursive elimination of features. We present theoretical properties of this method and show that it is uniformly consistent in finding the correct feature space under certain generalized assumptions. We present a few case studies to show that the assumptions are met in most practical situations and present simulation results to demonstrate performance of the proposed approach.

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中等高维内核机中的特征消除。

我们开发了一种基于递归特征消除的核机统计学习特征消除方法。我们给出了该方法的理论性质，并证明了在一定的广义假设下，该方法在寻找正确的特征空间方面是一致的。我们提出了一些案例研究，以表明这些假设在大多数实际情况下都是满足的，并给出了仿真结果来证明所提出方法的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Annals of Statistics 数学-统计学与概率论

CiteScore

9.30

自引率

8.90%

发文量

119

审稿时长

6-12 weeks

期刊介绍： The Annals of Statistics aim to publish research papers of highest quality reflecting the many facets of contemporary statistics. Primary emphasis is placed on importance and originality, not on formalism. The journal aims to cover all areas of statistics, especially mathematical statistics and applied & interdisciplinary statistics. Of course many of the best papers will touch on more than one of these general areas, because the discipline of statistics has deep roots in mathematics, and in substantive scientific fields.