NWFE and KNWFE-1

Nonparametric Weighted Feature Extraction (NWFE)

Abstract:
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.
Index Terms—Dimensionality reduction, discriminant analysis, nonparametric feature extraction.

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Kernel Nonparametric Weighted Feature Extraction (KNWFE)

Abstract:
In the recent years, many studies show that kernel methods are computationally efficient, robust, and stable for pattern analysis. Many kernel-based classifiers were designed and applied to classify remote-sensed data and some results show that kernel-based classifiers have satisfying performances. Many researches about hyperspectral image classification also show that nonparametric weighted feature extraction (NWFE) is a powerful tool for extracting hyperspectral image features. But NWFE is still based on linear transformation. In this paper, kernel method is applied to extend NWFE to kernel-based NWFE (KNWFE). The new KNWFE possesses the advantages of both linear and nonlinear transformation and the experimental results show that KNWFE outperforms NWFE, DBFE, ICA , KPCA, and GDA.

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External Link: http://kbc.ntcu.edu.tw/

Category:

Education

Tags:

• Kernel
• Nonparametric
• Weighted
• Feature
• Extraction
• NWFE
• KNWFE