Optimizing Kernel PCA Using Sparse Representation-Based Classifier for MSTAR SAR Image Target Recognition

Chuang Lin,Meng Pang,Binghui Wang,Xuefeng Zhao

doi:10.1155/2013/847062

Chuang Lin, Meng Pang + Show 2 more

Open Access

https://doi.org/10.1155/2013/847062

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Abstract

Different kernels cause various class discriminations owing to their different geometrical structures of the data in the feature space. In this paper, a method of kernel optimization by maximizing a measure of class separability in the empirical feature space with sparse representation-based classifier (SRC) is proposed to solve the problem of automatically choosing kernel functions and their parameters in kernel learning. The proposed method first adopts a so-called data-dependent kernel to generate an efficient kernel optimization algorithm. Then, a constrained optimization function using general gradient descent method is created to find combination coefficients varied with the input data. After that, optimized kernel PCA (KOPCA) is obtained via combination coefficients to extract features. Finally, the sparse representation-based classifier is used to perform pattern classification task. Experimental results on MSTAR SAR images show the effectiveness of the proposed method.

Highlights

Kernel learning or kernel machine has aroused broad interest in pattern recognition and kernel learning areas
The second experiment is carried out on MSTAR SAR images using kernel optimized PCA with sparse representation classifier (KOPCA) compared with conventional KPCA to extract features and use nearest neighbor (NN) classifier to implement pattern classification
Sparse representation-based classifier (SRC) is applied to verify its superiority and effectiveness to deal with pattern classification compared with other classifiers

Summary

Introduction

Kernel learning or kernel machine has aroused broad interest in pattern recognition and kernel learning areas. Considering that optimized kernel parameters of kernel function cannot change the geometrical structures of kernel in the feature space [1, 2], so it cannot improve the performance of kernel learning In this sense, Scholkopf et al [3] proposed an empirical kernel map which maps original input data space into a subspace of the empirical feature space.

Kernel Optimization in the Empirical Feature Space

Experimental Results

Conclusion