Kernel nonparametric weighted feature extraction for classification

被引：0
|
作者：
Kuo, BC
Li, CH
机构：
[1] Natl Taichung Univ, Grad Sch Educ Measurements & Stat, Taichung, Taiwan
[2] Natl Chung Hsing Univ, Dept Appl Math, Taichung 40227, Taiwan
来源：
AI 2005: ADVANCES IN ARTIFICIAL INTELLIGENCE | 2005年 / 3809卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Usually feature extraction is applied for dimension reduction in hyperspectral data classification problems. Many researches show that nonparametric weighted feature extraction (NWFE) is a powerful tool for extracting hyperspectral image features and kernel-based methods are computationally efficient, robust and stable for pattern analysis. In this paper, a kernel-based NWFE is proposed and a real data experiment is conducted for evaluating its performance. The experimental result shows that the proposed method outperforms original NWFE when the size training samples is large enough.
引用
收藏
页码：567 / 576
页数：10
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