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489
Kernel PCA for novelty detection
- Pattern Recognition
"... Kernel principal component analysis (kernel PCA) is a non-linear extension of PCA. This study introduces and investigates the use of kernel PCA for novelty detection. Training data are mapped into an infinite-dimensional feature space. In this space, kernel PCA extracts the principal components of t ..."
Abstract
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Cited by 52 (1 self)
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Kernel principal component analysis (kernel PCA) is a non-linear extension of PCA. This study introduces and investigates the use of kernel PCA for novelty detection. Training data are mapped into an infinite-dimensional feature space. In this space, kernel PCA extracts the principal components
Fast iterative kernel PCA
- Advances in Neural Information Processing Systems
, 2007
"... We introduce two methods to improve convergence of the Kernel Hebbian Algo-rithm (KHA [1]) for iterative kernel PCA. KHA has a scalar gain parameter which is either held constant or decreased as 1/t, leading to slow convergence. Our KHA/et algorithm accelerates KHA by incorporating the reciprocal of ..."
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Cited by 4 (1 self)
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We introduce two methods to improve convergence of the Kernel Hebbian Algo-rithm (KHA [1]) for iterative kernel PCA. KHA has a scalar gain parameter which is either held constant or decreased as 1/t, leading to slow convergence. Our KHA/et algorithm accelerates KHA by incorporating the reciprocal
Kernel PCA for Feature . . .
- NEURAL COMPUT APPLIC (2001)10:231--243
, 2001
"... In this paper, we propose the application of the Kernel Principal Component Analysis (PCA) technique for feature selection in a high-dimensional feature space, where input variables are mapped by a Gaussian kernel. The extracted features are employed in the regression problems of chaotic Mackey-G ..."
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In this paper, we propose the application of the Kernel Principal Component Analysis (PCA) technique for feature selection in a high-dimensional feature space, where input variables are mapped by a Gaussian kernel. The extracted features are employed in the regression problems of chaotic Mackey
Kernel PCA for Image Compression
, 2006
"... Im Bereich des maschinellen Lernens haben sich kernelbasierte Methoden als sehr erfolgreich erwiesen. Die Anwendung des sogenannten Kernel-Tricks ermöglicht die Ausführung linearer Algorithmen in hochdimensionalen Vektorräumen durch implizite nichtlineare Abbildungen. Erfolgreich angewandt wurde auc ..."
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auch das kernelbasierte Äquivalent der Hauptkomponentenanalyse (Principal Component Analysis, PCA), die sogenannte Kernel-PCA. Anwendungen zum Entrauschen von Bildern und zur Rekonstruktion hochaufgelöster Bilder aus unterabgetasteten Näherungen zeigen bei Vergleichen mit linearer PCA die überlegene
Stochastic Optimization for Kernel PCA∗
"... Kernel Principal Component Analysis (PCA) is a popular ex-tension of PCA which is able to find nonlinear patterns from data. However, the application of kernel PCA to large-scale problems remains a big challenge, due to its quadratic space complexity and cubic time complexity in the number of ex-amp ..."
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Kernel Principal Component Analysis (PCA) is a popular ex-tension of PCA which is able to find nonlinear patterns from data. However, the application of kernel PCA to large-scale problems remains a big challenge, due to its quadratic space complexity and cubic time complexity in the number of ex
DETECTING INFLUENTIAL OBSERVATIONS IN KERNEL PCA
, 2008
"... Individual observations can be very influential when performing classical Principal Component Analysis in a Euclidean space. Robust PCA algorithms detect and neutralize such dominating data points. This paper studies robustness issues for PCA in a kernel induced feature space. The sensitivity of Ker ..."
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Cited by 1 (0 self)
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Individual observations can be very influential when performing classical Principal Component Analysis in a Euclidean space. Robust PCA algorithms detect and neutralize such dominating data points. This paper studies robustness issues for PCA in a kernel induced feature space. The sensitivity
Missing Data in Kernel PCA
, 2006
"... Kernel Principal Component Analysis (KPCA) is a widely used technique for visualisation and feature extraction. Despite its success and flexibility, the lack of a probabilistic interpretation means that some problems, such as handling missing or corrupted data, are very hard to deal with. In this pa ..."
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Cited by 6 (0 self)
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Kernel Principal Component Analysis (KPCA) is a widely used technique for visualisation and feature extraction. Despite its success and flexibility, the lack of a probabilistic interpretation means that some problems, such as handling missing or corrupted data, are very hard to deal with
Results 1 - 10
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489