| Nonlinear discriminant analysis based on vanishing component analysis | |
Shao, Yunxue1; Gao, Guanglai1; Wang, Chunheng2
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| 发表期刊 | NEUROCOMPUTING
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| 2016-12-19 | |
| 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 |
| DOI | 10.1016/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 |
| 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. |
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| 1-s2.0-S092523121630（840KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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