Results 251 to 260 of about 26,356,550 (281)

Anomaly Detection Based on Kernel Principal Component and Principal Component Analysis

2018
Nowadays, behind wall human detection based on UWB radar signal, which it had a strong anti-jamming performance, was an important problem. In this setting, principal component analysis (PCA) as an anomaly detection method was used, but PCA could only deal with linear data.
Wei Wang 0180, Min Zhang 0017, Dan Wang, Yu Jiang, Yuliang Li, Hongda Wu   +5 more
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Accuracy of suboptimal solutions to kernel principal component analysis

Computational Optimization and Applications, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Giorgio Gnecco, Marcello Sanguineti
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Face Recognition with Weighted Kernel Principal Component Analysis

2006 9th International Conference on Control, Automation, Robotics and Vision, 2006
Principal component analysis (PCA) is one of the most traditional linear dimensionality reduction algorithms. Kernel principal component analysis (Kernel PCA), generalization of PCA, is a nonlinear feature extraction method. However, both PCA and Kernel PCA are lack of class information in their feature subspace.
Nan Liu 0003, Han Wang 0001, Wei-Yun Yau
openaire   +1 more source

Kernelized linear principal component discriminant analysis

Neural Networks
In this paper, we propose Kernelized Linear Principal Component Discriminant Analysis (KLPCDA), a structured and unified framework for discriminant analysis that overcomes the fragmentation in existing multi-stage approaches such as PCA+LDA or KPCA+GDA. Instead of treating feature extraction and class discrimination as disjoint steps, KLPCDA formulates
Lingxiao Qu, Yan Pei 0001
openaire   +3 more sources

Spherical coordinate-based kernel principal component analysis

Signal, Image and Video Processing, 2020
This 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
openaire   +2 more sources

Projection-free kernel principal component analysis for denoising

Neurocomputing, 2019
Abstract Kernel principal component analysis (KPCA) forms the basis for a class of methods commonly used for denoising a set of multivariate observations. Most KPCA algorithms involve two steps: projection and preimage approximation. We argue that this two-step procedure can be inefficient and result in poor denoising.
Anh Tuan Bui, Joon-Ku Im, Daniel W. Apley, George C. Runger   +3 more
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A comparison of sparse kernel principal component analysis methods

KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516), 2002
Presents a comparative study of a group of methods based on kernels which attempt to identify only the most significant cases with which to create the nonlinear feature space. Kernels were originally derived in the context of support vector machines, which identify the smallest number of data points necessary to solve a particular problem (e.g ...
Zhen Kun Gon, JunKang Feng, Colin Fyfe
openaire   +2 more sources

Subset kernel principal component analysis

2009 IEEE International Workshop on Machine Learning for Signal Processing, 2009
Kernel 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.
openaire   +1 more source

Application of Kernel Principal Components Analysis to pattern recognitions

Proceedings of the 41st SICE Annual Conference. SICE 2002., 2003
Kernel Principal Component Analysis (Kernel PCA) is one of the methods to perform PCA in high dimensional space. The purpose of this paper is to examine what components are obtained by Kernel PCA and evaluate effectiveness of the components as feature. Simulation's results show that Kernel PCA can get superior performance to PCA.
Sohara, Kosuke, Kotani, Manabu
openaire   +1 more source

Approximations of the standard principal components analysis and kernel PCA

Expert Systems with Applications, 2010
Principal component analysis (PCA) is a powerful technique for extracting structure from possibly high-dimensional data sets, while kernel PCA (KPCA) is the application of PCA in a kernel-defined feature space. For standard PCA and KPCA, if the size of dataset is large, it will need a very large memory to store kernel matrix and a lot of time to ...
Rui Zhang, Wenjian Wang, Yi-Chen Ma
openaire   +2 more sources