The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Kernel Matched Subspace Detectors for Hyperspectral Target Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Multi-category classification by kernel based nonlinear subspace method
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
The CFAR adaptive subspace detector is a scale-invariant GLRT
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
EURASIP Journal on Advances in Signal Processing
Recent advances in remote sensing image processing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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Several linear and nonlinear detection algorithms that are based on spectral matched (subspace) filters are compared. Nonlinear (kernel) versions of these spectral matched detectors are also given and their performance is compared with linear versions. Several well-known matched detectors such as matched subspace detector, orthogonal subspace detector, spectral matched filter, and adaptive subspace detector are extended to their corresponding kernel versions by using the idea of kernel-based learning theory. In kernel-based detection algorithms the data is assumed to be implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel function. The expression for each detection algorithm is then derived in the feature space, which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high-dimensional feature space. Experimental results based on simulated toy examples and real hyperspectral imagery show that the kernel versions of these detectors outperform the conventional linear detectors.