Applying Composite Kernel to Kernel-based Nonparametric Weighted Feature Extraction

被引：0
|
作者：
Huang, Chih-Sheng [1 ]
Li, Cheng-Hsuan [1 ]
Lin, Shih-Syun
Kuo, Bor-Chen [1 ]
机构：
[1] Natl Taichung Univ, Grad Sch Educ Measurement & Stat, Taichung, Taiwan
来源：
ICIEA 2010: PROCEEDINGS OF THE 5TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, VOL 4 | 2010年
关键词：
NWFE; KNWFE; composite kernel; CLASSIFICATION;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In the recent researches show that nonparametric weighted feature extraction (NWFE) is a useful method for extracting hyperspectral image features. Kernel-based NWFE (KNWFE) is applying the kernel method to extend the more effective projected features in the feature space. It had been showed the performance of KNWFE is better than NWFE. In this study, we would apply a composite kernel function with spectral and spatial information to KNWFE, and hope this composite kernel to KNWFE can get a better performance than the spectral-based kernel function to KNWFE. In the experiment results show that the KNWFE with composite kernel, include the spectral and spatial information, outperforms the KNWFE with the only spectral based kernel function
引用
收藏
页码：40 / +
页数：2
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