Nonlinear discriminant analysis based on vanishing component analysis
Shao, Yunxue1; Gao, Guanglai1; Wang, Chunheng2
Source PublicationNEUROCOMPUTING
2016-12-19
Volume218Pages:172-184
SubtypeArticle
AbstractMost kernel-based nonlinear discriminant analysis methods need to compute the kernel distance between test samples and all of the training samples, but this approach consumes large volumes of time and memory, and it may be impractical when there is a large number of training samples. In this study, we propose a vanishing component analysis (VCA) based nonlinear discriminant analysis (VNDA) method. First, VNDA learns nonlinear mapping functions explicitly using the modified VCA method, before employing these functions to map the input feature onto a high-dimensional polynomial feature space, where the linear discriminant analysis (LDA) method is then applied. We prove that principal components analysis plus LDA is a special case of VNDA and that the set of mapping functions learned by VNDA is the best solution to the ratio trace problem in. the degree bounded polynomial feature space. Unlike kernel-based methods, VNDA only stores these mapping functions instead of all the training samples in the test step. Experimental results obtained based on four simulated data sets and 15 real data sets demonstrate that the proposed method yields highly competitive test recognition results compared to the state-of-the-art methods, while consuming less memory and time resources. (C) 2016 Elsevier B.V. All rights reserved.
KeywordKernel Discriminant Analysis Linear Discriminant Analysis Vanishing Component Analysis Support Vector Machine Random Forest
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2016.08.058
WOS KeywordRECOGNITION ; DECOMPOSITION ; FORESTS
Indexed BySCI
Language英语
Funding Organizationprogram of higher-level talents of Inner Mongolia University(135137) ; National Natural Science Foundation of China (NSFC)(61563039 ; 61531019)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000388053700019
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13348
Collection复杂系统管理与控制国家重点实验室_影像分析与机器视觉
Affiliation1.Inner Mongolia Univ, Coll Comp Sci, Hohhot, Inner Mongolia, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China
Recommended Citation
GB/T 7714
Shao, Yunxue,Gao, Guanglai,Wang, Chunheng. Nonlinear discriminant analysis based on vanishing component analysis[J]. NEUROCOMPUTING,2016,218:172-184.
APA Shao, Yunxue,Gao, Guanglai,&Wang, Chunheng.(2016).Nonlinear discriminant analysis based on vanishing component analysis.NEUROCOMPUTING,218,172-184.
MLA Shao, Yunxue,et al."Nonlinear discriminant analysis based on vanishing component analysis".NEUROCOMPUTING 218(2016):172-184.
Files in This Item: Download All
File Name/Size DocType Version Access License
1-s2.0-S092523121630(840KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Shao, Yunxue]'s Articles
[Gao, Guanglai]'s Articles
[Wang, Chunheng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Shao, Yunxue]'s Articles
[Gao, Guanglai]'s Articles
[Wang, Chunheng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Shao, Yunxue]'s Articles
[Gao, Guanglai]'s Articles
[Wang, Chunheng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 1-s2.0-S0925231216309547-main.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.