Kernel nonparametric weighted feature extraction for classification

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.