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Iterative kernel principal component analysis for image modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005In 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.
Kim, KI, Franz, MO, Scholkopf, B
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Penalized Preimage Learning in Kernel Principal Component Analysis
IEEE Transactions on Neural Networks, 2010Finding the preimage of a feature vector in kernel principal component analysis (KPCA) is of crucial importance when KPCA is applied in some applications such as image preprocessing. Since the exact preimage of a feature vector in the kernel feature space, normally, does not exist in the input data space, an approximate preimage is learned and ...
Wei-Shi, Zheng +2 more
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Robust Kernel Principal Component Analysis
2018Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to higher (usually) dimensional feature space where the data can be linearly modeled.
Nguyen, Minh Hoai, Torre, Fernando De La
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Spherical coordinate-based kernel principal component analysis
Signal, Image and Video Processing, 2020This paper proposes a spherical coordinate-based kernel principal component analysis (PCA). Here, the kernel function is the nonlinear transform from the Cartesian coordinate system to the spherical coordinate system. In particular, first, the vectors represented in the Cartesian coordinate system are expressed as those represented in the spherical ...
Yitong Guo, Bingo Wing-Kuen Ling
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Tangent Hyperplane Kernel Principal Component Analysis for Denoising
IEEE Transactions on Neural Networks and Learning Systems, 2012Kernel principal component analysis (KPCA) is a method widely used for denoising multivariate data. Using geometric arguments, we investigate why a projection operation inherent to all existing KPCA denoising algorithms can sometimes cause very poor denoising.
Joon-Ku, Im +2 more
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Greedy Kernel Principal Component Analysis
2006This contribution discusses one aspect of statistical learning and generalization. The theory of learning is very relevant to cognitive systems including cognitive vision.
Vojtěch Franc, Václav Hlaváč
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Kernel $$\ell ^1$$-norm principal component analysis for denoising
Optimization Letters, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiao Ling, Anh Bui, Paul Brooks
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Subset kernel principal component analysis
2009 IEEE International Workshop on Machine Learning for Signal Processing, 2009Kernel principal component analysis (kernel PCA or KPCA) has been used widely for non-linear feature extraction, dimensionally reduction, and classification problems. However, KPCA is known to have high computational complexity, that is the eigenvalue decomposition of which size equals to the number of samples n.
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Robust kernel principal component analysis and classification
Advances in Data Analysis and Classification, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Debruyne, Michiel, Verdonck, Tim
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Kernel principal component analysis for change detection
SPIE Proceedings, 2008Principal component analysis (PCA) is often used to detect change over time in remotely sensed images. A commonly used technique consists of finding the projections along the two eigenvectors for data consisting of two variables which represent the same spectral band covering the same geographical region acquired at two different time points.
Allan A. Nielsen, Morton J. Canty
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