带再现核的非线性顶点判别分析

IF 2.1 4区数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Statistical Analysis and Data Mining Pub Date : 2012-04-01 DOI:10.1002/sam.11137

Tong Tong Wu, Yichao Wu

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引用次数: 0

摘要

顶点判别分析（VDA）这种新颖的监督学习方法在多类别分类中表现出色。本文探讨了 VDA 在非线性判别方面的应用。通过加入再现核，VDA 可以从线性判别推广到非线性判别。我们的数值实验表明，基于再现核的新方法可以对线性和非线性情况进行准确分类。

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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Nonlinear Vertex Discriminant Analysis with Reproducing Kernels.

The novel supervised learning method of vertex discriminant analysis (VDA) has been demonstrated for its good performance in multicategory classification. The current paper explores an elaboration of VDA for nonlinear discrimination. By incorporating reproducing kernels, VDA can be generalized from linear discrimination to nonlinear discrimination. Our numerical experiments show that the new reproducing kernel-based method leads to accurate classification for both linear and nonlinear cases.

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来源期刊

Statistical Analysis and Data Mining COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.20

自引率

7.70%

发文量

期刊介绍： Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce. The focus of the journal is on papers which satisfy one or more of the following criteria: Solve data analysis problems associated with massive, complex datasets Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research. Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models Provide survey to prominent research topics.