Iterative kernel principal component analysis for image modeling

@article{Kim2005IterativeKP,
  title={Iterative kernel principal component analysis for image modeling},
  author={Kwang In Kim and Matthias O. Franz and Bernhard Sch{\"o}lkopf},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2005},
  volume={27},
  pages={1351-1366}
}
In recent years, kernel principal component analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the kernel Hebbian algorithm, which iteratively estimates the kernel principal components… CONTINUE READING
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