Results 161 to 170 of about 29,752 (189)

A Recursive Online Kernel PCA Algorithm

2010 20th International Conference on Pattern Recognition, 2010
In this paper, we describe a new method for performing kernel principal component analysis which is online and also has a fast convergence rate. The method follows the Rayleigh quotient to obtain a fixed point update rule to extract the leading eigenvalue and eigenvector. Online deflation is used to estimate the remaining components.
Erion Hasanbelliu, Luis Gonzalo Sánchez Giraldo, José C. Príncipe   +2 more
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PCA and kernel PCA

2014
Introduction Two primary techniques for dimension-reducing feature extraction are subspace projection and feature selection . This chapter will explore the key subspace projection approaches, i.e. PCA and KPCA. (i) Section 3.2 provides motivations for dimension reduction by pointing out (1) the potential adverse effect of large feature ...
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Centered Subset Kernel PCA for Denoising

2011
Kernel PCA has been applied to image processing, even though, it is known to have high computational complexity. We introduce centered Subset KPCA for image denoising problems. Subset KPCA has been proposed for reduction of computational complexity of KPCA, however, it does not consider a pre-centering that is often important for image processing ...
Yoshikazu Washizawa, Masayuki Tanaka 0001   +1 more
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A general kernelization framework for learning algorithms based on kernel PCA

Neurocomputing, 2010
In this paper, a general kernelization framework for learning algorithms is proposed via a two-stage procedure, i.e., transforming data by kernel principal component analysis (KPCA), and then directly performing the learning algorithm with the transformed data. It is worth noting that although a very few learning algorithms were also kernelized by this
Changshui Zhang, Feiping Nie 0001, Shiming Xiang   +2 more
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Subset kernel PCA for pattern recognition

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009
Subspace methods that utilize principal component analysis (PCA) are widely used for pattern classification or detection problems. Kernel PCA (KPCA) that is an extension of PCA is also applied to subspace methods. However, its computational cost is very high since the computational cost mainly depends on the number of samples in kernel methods ...
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The Power Load Forecasting by Kernel PCA

2010
We use one year's subset to train the Support Vector Machines (SVM) and the next year's data was used for testing with Kernel Principal Components Analysis (KPCA). This is clearly not optimal for a non-stationary time series such as we have here nevertheless the MAPE of peak load data set with back-propagation neural network [Chuang et al., 1998] is 3 ...
Fang-Tsung Liu, Chiung-Hsing Chen, Shang-Jen Chuang, Ting-Chia Ou   +3 more
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A robust kernel PCA algorithm

Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826), 2005
This paper presents a novel algorithm - robust kernel principal component analysis (robust KPCA), on the basis of the research of kernel principal component analysis (KPCA) and robust principal component analysis (RPCA). First, this algorithm sets the radius of the images of the training samples in the feature space using kernel tricks, then determines
null Cong-de Lu, null Tai-yi Zhang, null Xing-Zhong Du, null Can-ping Li   +3 more
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Robust De-noising by Kernel PCA

2002
Recently, kernel Principal Component Analysis is becoming a popular technique for feature extraction. It enables us to extract nonlinear features and therefore performs as a powerful preprocessingstep for classification. There is one drawback, however, that extracted feature components are sensitive to outliers contained in data.
Takashi Takahashi, Takio Kurita
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Insights into Kernel PCA with Application to Multivariate Extremes

SIAM Journal on Mathematics of Data Science
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Marco Avella Medina, Richard A. Davis, Gennady Samorodnitsky   +2 more
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A Pattern Selection Algorithm in Kernel PCA Applications

2008
Principal Component Analysis (PCA) has been extensively used in different fields including earth science for spatial pattern identification. However, the intrinsic linear feature associated with standard PCA prevents scientists from detecting nonlinear structures. Kernel-based principal component analysis (KPCA), a recently emerging technique, provides
Ruixin Yang, John Tan, Menas Kafatos
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