Sharp-SSL：用于半监督学习的选择性高维轴对齐随机投影

IF 3 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association Pub Date : 2024-04-12 DOI:10.1080/01621459.2024.2340792

Tengyao Wang, Edgar Dobriban, Milana Gataric, Richard J. Samworth

引用次数: 0

摘要

我们提出了一种解决高维半监督学习问题的新方法，该方法基于对应用于许多轴对齐随机原型的低维程序结果的仔细汇总。

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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Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning

We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-aligned random pro...

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来源期刊

Journal of the American Statistical Association 数学-统计学与概率论

CiteScore

7.50

自引率

8.10%

发文量

168

审稿时长

12 months

期刊介绍： Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement are reviewed in JASA . JASA is indexed in Current Index to Statistics and MathSci Online and reviewed in Mathematical Reviews. JASA is abstracted by Access Company and is indexed and abstracted in the SRM Database of Social Research Methodology.