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Nonlinear discriminant analysis based on vanishing component analysis

文献类型：期刊论文

作者Shao, Yunxue1; Gao, Guanglai1; Wang, Chunheng2
刊名NEUROCOMPUTING
出版日期2016-12-19
卷号218页码:172-184
关键词Kernel Discriminant Analysis Linear Discriminant Analysis Vanishing Component Analysis Support Vector Machine Random Forest
DOI10.1016/j.neucom.2016.08.058
文献子类Article
英文摘要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.
WOS关键词RECOGNITION ; DECOMPOSITION ; FORESTS
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000388053700019
资助机构program of higher-level talents of Inner Mongolia University(135137) ; National Natural Science Foundation of China (NSFC)(61563039 ; 61531019)
源URL[http://ir.ia.ac.cn/handle/173211/13348]  
专题自动化研究所_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团队
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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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