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kernel PCA

by Takahiro Ogawa
"... Adaptive example-based super-resolution using ..."
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Adaptive example-based super-resolution using

Kernel PCA for novelty detection

by Heiko Hoffmann - 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 - Cited by 51 (1 self) - Add to MetaCart
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

Kernel PCA for Feature . . .

by Roman Rosipal, Mark Girolami, Leonard J. Trejo, Andrzej Cichocki - 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

Fast iterative kernel PCA

by Nicol N. Schraudolph, S. V. N. Vishwanathan - 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 ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
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 Image Compression

by Benjamin Huhle , 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∗

by Lijun Zhang, Tianbao Yang, Jinfeng Yi, Rong Jin, Zhi-hua Zhou
"... 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

by Van Horebeek, Michiel Debruyne, Mia Hubert , 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 ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
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

by Guido Sanguinetti, Neil D. Lawrence , 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 ..."
Abstract - Cited by 6 (0 self) - Add to MetaCart
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

Kernel PCA and De-Noising in Feature Spaces

by Sebastian Mika , Bernhard Schölkopf, Alex Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch - ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 11 , 1999
"... Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classification algorithms. But it can also be considered as a natural generalization of linear principal component analysis. This gives rise to the question how to use nonlinear features for data compress ..."
Abstract - Cited by 169 (15 self) - Add to MetaCart
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classification algorithms. But it can also be considered as a natural generalization of linear principal component analysis. This gives rise to the question how to use nonlinear features for data

Kernel PCA: 1

by Le Song, ,
"... dependence ..."
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dependence
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