Golden Ratio-Based Sufficient Dimension Reduction

IF 2.9 3区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Wenjing Yang;Yuhong Yang
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引用次数: 0

Abstract

Many machine learning applications deal with high-dimensional data. To make computations feasible and learning more efficient, it is often desirable to reduce the dimensionality of the input variables by finding linear combinations of the predictors that can retain as much original information as possible in the relationship between the response and the original predictors. We propose a neural network-based sufficient dimension reduction method that not only identifies the structural dimension effectively, but also improves the estimation accuracy on the central space. It takes advantage of approximation capabilities of neural networks for functions in some Barron classes and leads to reduced computation cost compared to other dimension reduction methods in the literature. Additionally, the framework can be extended to fit practical dimension reduction, making the methodology more applicable in practical settings.
基于黄金比例的充分降维
许多机器学习应用程序处理高维数据。为了使计算可行和学习更有效,通常需要通过寻找预测器的线性组合来降低输入变量的维数,这些预测器可以在响应和原始预测器之间的关系中保留尽可能多的原始信息。提出了一种基于神经网络的充分降维方法,该方法不仅有效地识别了结构维数,而且提高了对中心空间的估计精度。它利用了神经网络对某些巴伦类函数的逼近能力,与文献中其他降维方法相比,降低了计算成本。此外,该框架可以扩展以适应实际降维,使该方法更适用于实际设置。
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
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory 工程技术-工程:电子与电气
CiteScore
5.70
自引率
20.00%
发文量
514
审稿时长
12 months
期刊介绍: The IEEE Transactions on Information Theory is a journal that publishes theoretical and experimental papers concerned with the transmission, processing, and utilization of information. The boundaries of acceptable subject matter are intentionally not sharply delimited. Rather, it is hoped that as the focus of research activity changes, a flexible policy will permit this Transactions to follow suit. Current appropriate topics are best reflected by recent Tables of Contents; they are summarized in the titles of editorial areas that appear on the inside front cover.
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