Nonlinear discriminant analysis based on vanishing component analysis
Shao, Yunxue1; Gao, Guanglai1; Wang, Chunheng2
2016-12-19
发表期刊NEUROCOMPUTING
卷号218页码:172-184
文章类型Article
摘要Most 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.
关键词Kernel Discriminant Analysis Linear Discriminant Analysis Vanishing Component Analysis Support Vector Machine Random Forest
WOS标题词Science & Technology ; Technology
DOI10.1016/j.neucom.2016.08.058
关键词[WOS]RECOGNITION ; DECOMPOSITION ; FORESTS
收录类别SCI
语种英语
项目资助者program of higher-level talents of Inner Mongolia University(135137) ; National Natural Science Foundation of China (NSFC)(61563039 ; 61531019)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000388053700019
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/13348
专题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
作者单位1.Inner Mongolia Univ, Coll Comp Sci, Hohhot, Inner Mongolia, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China
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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.
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