Results 171 to 180 of about 7,354 (203)

Feature extraction via KPCA for classification of gait patterns

Human Movement Science, 2007
Automated recognition of gait pattern change is important in medical diagnostics as well as in the early identification of at-risk gait in the elderly. We evaluated the use of Kernel-based Principal Component Analysis (KPCA) to extract more gait features (i.e., to obtain more significant amounts of information about human movement) and thus to improve ...
Jianning, Wu, Jue, Wang, Li, Liu
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Fault detection of chiller based on improved KPCA

2016 Chinese Control and Decision Conference (CCDC), 2016
Principal component analysis (PCA) is a common fault detection method. But it is difficult to get high accuracy· if it is applied to complex nonlinear system. Faced with complex system such as chiller· this paper proposes using kernel principal component analysis (KPCA) for fault detection.
Nanhua Zhang, Xuejin Gao, Yafen Li, Pu Wang   +3 more
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KDA Plus KPCA for Face Recognition

2006
Kernel discriminant analysis (KDA) and the kernel principal component analysis (KPCA), which are the extension of the linear discriminant analysis (LDA) and the principal component analysis (PCA), respectively, from linear domain to nonlinear domain via the kernel trick, are two very popular nonlinear feature extraction methods.
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Distribution Centers Site Selection Based on KPCA-SVRM

2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008
Distribution centers site selection has become a popular problem in recent years. Fine distribution centers site selection can ensure the supply and reduce the cost. By studying the methods proposed by other scholars, a mew method, KPCA (kernel principal component analysis) -SVRM (support vector regression machine) is proposed by this paper.
Cai-Qing Zhang, Pan Lu, Ze-Jian Liu
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Face recognition base on KPCA with polynomial kernels

2007 International Conference on Wavelet Analysis and Pattern Recognition, 2007
Kernel principal component analysis (KPCA), a improving of PCA, is used in face recognition. The paper describes the use of kernel principal component analysis with polynomial kernels to extracts face image features in high-dimensional spaces. KPCA extracts feature set more suitable for categorization than classical Principal Component Analysis does ...
null Li-Hong Zhao, null Xi-Li Zhang, null Xin-He Xu   +2 more
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CFAR and KPCA for SAR image target detection

2010 3rd International Congress on Image and Signal Processing, 2010
A SAR target detection model based on CFAR and KPCA is presented in this paper. This Detection is divided into a pre-screening and discrimination process. Within the large-scale and low-resolution SAR imagery, pre-screening adopts classic CFAR techniques, while the discrimination process adopts kernel principal component analysis to separate the target
WanJing Meng, Tao Ju, HongYun Yu
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Novel Nonlinear Process Monitoring Based on KPCA-ICA

Advanced Materials Research, 2012
A novel nonlinear process monitoring method based on kernel principal component analysis (KPCA) - independent component analysis (ICA) is proposed. The new method is a two-phase algorithm: whitened KPCA plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined
Ying Wang Xiao, Chen Zhong Zhang
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Improved Indoor positioning algorithm using KPCA and ELM

2019 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019
RSS (Received Signal Strength) values which are used in indoor position estimation based on fingerprinting are affected by noise. The RSS value received by the fixed line-of-sight condition points obeys the Gauss distribution and does not match the Gauss distribution. WIFI signal transmission attenuation is also a nonlinear attenuation.
Lijun Lian, Shaobo Xia, Sen Zhang, Qiong Wu, Changqiang Jing   +4 more
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Kernel optimisation for KPCA based on Gaussianity estimation

International Journal of Bio-Inspired Computation, 2014
Kernel-based principle component analysis KPCA is an effective feature extraction method. It extends PCA to nonlinear cases using kernel trick. The performance of KPCA relies on the pre-selected parameter of kernel function. In this paper, we propose a kernel parameter optimisation method by using principle component subspace-based Gaussianity ...
Qi Kang, Kang Wang, Bingyao Huang, Jing An   +3 more
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Computerized Wrist Pulse Signal Diagnosis Using KPCA

2010
Wrist pulse signals can reflect the pathological changes of a person's body condition due to the richness and importance of the contained information. In recent years, the computerized pulse signal analysis has shown a great potential to the modernization of traditional pulse diagnosis.
Yunlian Sun, Bo Shen, Yinghui Chen, Yong Xu   +3 more
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