Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Non-linear Wiener filter in reproducing kernel Hilbert space
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Relative Karhunen-Loeve transform
IEEE Transactions on Signal Processing
Kernel Wiener filter and its application to pattern recognition
IEEE Transactions on Neural Networks
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Wiener filter is used widely for the inverse problem. From an observed signal, it provides the best restored signal with respect to the squared error averaged over the original signal and the noise among linear operators. In this paper, we propose applying the kernel Wiener filter, which enables us to handle signals non-linearly by mapping signals to the high dimensional space with kernel trick, to the pattern recognition problem. We regard a pattern as an observed signal and provide an identical original vector for the patterns belonging to the same class. Finally we classify an unknown pattern into the class of which vector is the nearest to the restored signal in the high dimensional space of the original space. In addition, we apply linear approximation to the kernel function to enable the regularization based on the distance in the observed signal space to enhance its performance of generalization. And also we adjust the value of the kernel function in the high dimensional original signal space to improve its ability further more.