Results 241 to 250 of about 26,356,550 (281)
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Evolving kernel principal component analysis for fault diagnosis
Computers & Industrial Engineering, 2007Abstract Feature extraction is the core of a fault diagnosis system. This paper presents a novel approach, called evolving kernel principal component analysis (EKPCA), to transform the original features to a more effective nonlinear combination in fault classification.
Ruixiang Sun 0002 +2 more
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Improved kernel principal component analysis for fault detection
Expert Systems with Applications, 2008This paper improves kernel principal component analysis (KPCA) for fault detection from two aspects. Firstly, a feature vector selection (FVS) scheme based on a geometrical consideration is given to reduce the computational complexity of KPCA when the number of samples becomes large.
Peiling Cui, Junhong Li, Guizeng Wang
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Robust Kernel Principal Component Analysis
Neural Computation, 2009This letter discusses the robustness issue of kernel principal component analysis. A class of new robust procedures is proposed based on eigenvalue decomposition of weighted covariance. The proposed procedures will place less weight on deviant patterns and thus be more resistant to data contamination and model deviation. Theoretical influence functions
Su-Yun Huang, Yi-Ren Yeh, Shinto Eguchi
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Incremental Kernel Principal Component Analysis
IEEE Transactions on Image Processing, 2007The kernel principal component analysis (KPCA) has been applied in numerous image-related machine learning applications and it has exhibited superior performance over previous approaches, such as PCA. However, the standard implementation of KPCA scales badly with the problem size, making computations for large problems infeasible.
Tat-Jun Chin, David Suter
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A Robust Method for Kernel Principal Component Analysis
2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2020Kernel principal component analysis (KPCA) as a dimensionality reduction method has been applied in many applications. It is well known that the KPCA is formulated in a least square sense. However, it is sensitive to outlier. Since each principal axis in feature space is a linear combination of the mapped data, it could be distorted due to outlier ...
Duo Wang, Toshihisa Tanaka 0001
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Kernel principal component analysis for texture classification
IEEE Signal Processing Letters, 2001Kernel principal component analysis (PCA) has recently been proposed as a nonlinear extension of PCA. The basic idea is to first map the input space into a feature space via a nonlinear map and then compute the principal components in that feature space. This letter illustrates the potential of kernel PCA for texture classification.
K. I. Kim, S. H. Park, H. J. Kim
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An Improved Algorithm for Kernel Principal Component Analysis
Neural Processing Letters, 2005Kernel principal component analysis (KPCA), introduced by Scholkopf et al., is a nonlinear generalization of the popular principal component analysis (PCA) via the kernel trick. KPCA has shown to be a very powerful approach of extracting nonlinear features for classification and regression applications.
Wenming Zheng, Cairong Zou, Li Zhao 0003
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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.
Vojtech Franc, Václav Hlavác
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Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels
Sixth International Conference on Data Mining (ICDM'06), 2006Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples are known. In this paper, we propose an adaptive kernel selection method for kernel principal component analysis, which can effectively learn the kernels when the class labels ...
Daoqiang Zhang +2 more
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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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