.png){kind=icon}
返回
Adaptive quasiconformal kernel discriminant analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2007.10.003.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Kernel discriminant analysis (KDA) is effective to extract nonlinear discriminative features of input samples using the kernel trick. However, the conventional KDA algorithm endures the kernel selection which has significant impact on the performances of KDA. In order to overcome this limitation, a novel nonlinear feature extraction method called adaptive quasiconformal kernel discriminant analysis (AQKDA) is proposed in this paper. AQKDA maps the data from the original input space to the high dimensional kernel space using a quasiconformal kernel. The adaptive parameters of the quasiconformal kernel are automatically calculated through optimizing an objective function designed for measuring the class separability of data in the feature space. Consequently, the nonlinear features extracted by AQKDA have the larger class separability compared with KDA. Experimental results on the two real-world datasets demonstrate the effectiveness of the proposed method. (C) 2007 Elsevier B.V. All rights reserved.
Keyword:
kernel method
quasiconformal kernel
kernel discriminant analysis
adaptive quasiconformal kernel discriminant analysis
feature extraction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
A fast kernel-based nonlinear discriminant analysis for multi-class problems.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
Evaluation of charge transfer resistance by geometrical extrapolation of the centre of semicircular impedance diagrams通过半圆阻抗图中心的几何外推法评估电荷转移电阻
PREAI
分享收藏
查看更多内容