The nature of statistical learning theory
The nature of statistical learning theory
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Kernel independent component analysis
The Journal of Machine Learning Research
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
IEEE Transactions on Information Theory
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
A comparative analysis of kernel subspace target detectors for hyperspectral imagery
EURASIP Journal on Applied Signal Processing
Anomaly detection in hyperspectral imagery based on maximum entropy and nonparametric estimation
Pattern Recognition Letters
An MRF-based kernel method for nonlinear feature extraction
Image and Vision Computing
Kernel-based spectral matched signal detectors for hyperspectral target detection
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Kernel-based regularized-angle spectral matching for target detection in hyperspectral imagery
Pattern Recognition Letters
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In this paper, we present a kernel realization of a matched subspace detector (MSD) that is based on a subspace mixture model defined in a high-dimensional feature space associated with a kernel function. The linear subspace mixture model for the MSD is first reformulated in a high-dimensional feature space and then the corresponding expression for the generalized likelihood ratio test (GLRT) is obtained for this model. The subspace mixture model in the feature space and its corresponding GLRT expression are equivalent to a nonlinear subspace mixture model with a corresponding nonlinear GLRT expression in the original input space. In order to address the intractability of the GLRT in the feature space, we kernelize the GLRT expression using the kernel eigenvector representations as well as the kernel trick where dot products in the feature space are implicitly computed by kernels. The proposed kernel-based nonlinear detector, so-called kernel matched subspace detector (KMSD), is applied to several hyperspectral images to detect targets of interest. KMSD showed superior detection performance over the conventional MSD when tested on several synthetic data and real hyperspectral imagery.