Normalization and preimage problem in gaussian kernel PCA

Kernel PCA has received a lot of attention over the past years and showed usefull for many image processing problems. In this paper we analyse the issue of normalization in Kernel PCA for the pre-image problem. We present a geometric interpretation of the normalization process for the gaussian kerne...

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Main Authors: Thorstensen, N., Segonne, F., Keriven, R.
Format: Conference Proceeding
Language:English
Subjects:
Data analysis
Image denoising
Image processing
Image recognition
Kernel
Kernel PCA
Out-of-Sample
Pattern recognition
Principal component analysis
Shape
Signal reconstruction
Training data
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creator Thorstensen, N.
Segonne, F.
Keriven, R.
description Kernel PCA has received a lot of attention over the past years and showed usefull for many image processing problems. In this paper we analyse the issue of normalization in Kernel PCA for the pre-image problem. We present a geometric interpretation of the normalization process for the gaussian kernel. As a consequence, we could formulate a correct normalization criterion in centered feature space. Furthermore, we show how the proposed normalization criterion improves previous pre-image methods for the task of image denoising.
doi_str_mv 10.1109/ICIP.2008.4711861
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subjects Data analysis
Image denoising
Image processing
Image recognition
Kernel
Kernel PCA
Out-of-Sample
Pattern recognition
Principal component analysis
Shape
Signal reconstruction
Training data
title Normalization and preimage problem in gaussian kernel PCA
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