Hyperspectral data classification using nonparametric weighted feature extraction
暂无分享,去创建一个
In this paper, a new nonparametric feature extraction method is proposed for high dimensional multiclass pattern recognition problems. It is based on a nonparametric extension of scatter matrices. There are at least two advantages to using the proposed nonparametric scatter matrices. First, they are generally of full rank. This provides the ability to specify the number of extracted features desired and to reduce the effect of the singularity problem. This is in contrast to parametric discriminant analysis, which usually only can extract L-1 (number of classes minus one) features. In a real situation, this may not be enough. Second, the nonparametric nature of scatter matrices reduces the effects of outliers and works well even for non-normal data sets. The new method provides greater weight to samples near the expected decision boundary. This tends to provide for increased classification accuracy.
[1] Bor-Chen Kuo,et al. Improved statistics estimation and feature extraction for hyperspectral data classification , 2001 .
[2] Robert P. W. Duin,et al. Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[3] K. Fukunaga,et al. Nonparametric Discriminant Analysis , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Keinosuke Fukunaga,et al. Introduction to Statistical Pattern Recognition , 1972 .