Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Iterative Kernel Principal Component Analysis for Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
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Kernel PCA has been applied to image processing, even though, it is known to have high computational complexity. We introduce centered Subset KPCA for image denoising problems. Subset KPCA has been proposed for reduction of computational complexity of KPCA, however, it does not consider a pre-centering that is often important for image processing. Indeed, pre-centering of Subset KPCA is not straightforward because Subset KPCA utilizes two sets of samples. We propose an efficient algorithm for pre-centering, and provide an algorithm for preimage. Experimental results show that our method is comparable with a state-of-the-art image denoising method.