Results 161 to 170 of about 7,354 (203)

KPCA Plus FDA for Fault Detection

2007
Kernel principal component analysis (KPCA) is widely used for fault detection. In this paper, a KPCA plus Fisher discriminant analysis (FDA) scheme is adopted to improve the fault detection performance of KPCA. Simulation results are given to show the effectiveness of the improvements for fault detection performance in terms of high fault detection ...
Peiling Cui, Jiancheng Fang
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Combining KPCA and PSO for Pattern Denoising

2008 Chinese Conference on Pattern Recognition, 2008
KPCA based pattern denoising has been addressed. This method, based on machine learning, maps nonlinearly patterns in input space into a higher-dimensional feature space by kernel functions, then performs PCA in feature space to realize pattern denoising.
Jianwu Li, Lu Su
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Speech feature extraction method of improved KPCA

The 2nd International Conference on Information Science and Engineering, 2010
In this paper, we propose a novel speech feature extraction method using kernel principal component analysis (KPCA) based on kernel fuzzy K-means clustering. First, all frames of speech signal are divided into a given amount of clusters by kernel-based fuzzy K-means clustering and then features are extracted by KPCA, as a result of which the storage ...
Zhang Jun-chang, Chen Yuan-yuan
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Gender identification in face images using KPCA

2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 2009
The data in face images are distributed in a complex manner due to the variation of light intensity, facial expression and pose. In this paper the Kernel Principal Component Analysis (KPCA) is used to extract the feature set of male and female faces.
S Aji, T Jayanthi, M.R. Kaimal
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The facial expression recognition based on KPCA

2010 International Conference on Intelligent Control and Information Processing, 2010
Kernel Principal Component Analysis (KPCA) extracting principal component with nonlinear method is an improved PCA. The KPCA has been got widely used in feature extraction and face recognition. The KPCA can extract the feature set which is more suitable in categorization than the conventional PCA.
Yanmei Wang, Yanzhu Zhang
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Pedestrian Detection using KPCA and FLD Algorithms

2007 IEEE International Conference on Automation and Logistics, 2007
A pedestrian detection method by using kernel principle component analysis (KPCA) and Fisher linear discriminant (FLD) is presented in this paper. The basic idea of this method is to first utilize the KPCA algorithm to perform feature extraction, which obtains the nonlinear principle components in the high dimension feature space composed of haar ...
Ying-hong Liang, Zhi-yan Wang, Sen Guo, Xiao-wei Xu, Xiao-ye Cao   +4 more
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Gait recognition based on KPCA and KNN

2010 The 2nd Conference on Environmental Science and Information Application Technology, 2010
This paper presents a novel approach for human identification at a distance using gait recognition. The proposed work introduces a nonlinear machine learning method, Kernel Principal Component Analysis (KPCA), and K nearest neighbor classification (KNN) classifier for gait recognition.
null Ning Suo, null Xu Qian, null Jinhui Zhao   +2 more
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Greedy KPCA in Biomedical Signal Processing

2007
Biomedical signals are generally contaminated with artifacts and noise. In case artifacts dominate, the useful signal can easily be extracted with projective subspace techniques. Then, biomedical signals which often represent one dimensional time series, need to be transformed to multi-dimensional signal vectors for the latter techniques to be ...
Ana Rita Teixeira, Ana Maria Tomé, Elmar W. Lang   +2 more
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Face recognition based on KPCA and SVM

Proceeding of the 11th World Congress on Intelligent Control and Automation, 2014
KPCA algorithm can solve the problem of nonlinear characteristic that the PCA algorithm can't handle with and the traditional curvelet decomposition algorithm cannot take full advantage of the fine scale component information. So we put forward KPCA algorithm and data fusion algorithm. The KPCA algorithm has a good effect on extracting face contour and
null Jianhua Dong, null Jason Gu, null Xin Ma, null Yibin Li   +3 more
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An Efficient Person Name Bipolarization Using KPCA

2015 Fifth International Conference on Communication Systems and Network Technologies, 2015
Many of search engines are habitual to access these personal names aliases for betterment of search. Perfect recognition of existing article is valuable in diverse web related tasks like sentiment analysis, information reclamation, personal name disambiguation, and relation extraction. With the growth of web data many users try to share their knowledge
Shweta Nigam, Anand Jawdekar
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