Results 171 to 180 of about 3,257 (211)

Sparse KPCA for Feature Extraction in Speech Recognition

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006
This paper presents an analysis of the applicability of sparse kernel principal component analysis (SKPCA) for feature extraction in speech recognition, as well as a proposed approach to make the SKPCA technique realizable for a large amount of training data, which is a usual context in speech recognition systems.
Amaro A. de Lima, Heiga Zen, Yoshihiko Nankaku, Keiichi Tokuda, Tadashi Kitamura, Fernando Gil Resende   +5 more
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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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Fault Condition Recognition Based on PSO and KPCA

2009
A method of kernel principal component analysis (KPCA) based on particle swarm optimization (PSO) is presented, which is applied in fault condition recognition of gear box. Comprehensively considered within-class scatter and between-class scatter of samples feature, the fitness function of kernel function parameter optimized is constructed, and the ...
Hongxia Pan, Xiuye Wei, Xin Xu
openaire   +2 more sources

KPCA Feature Extraction Based on CBPSO

2009 International Workshop on Intelligent Systems and Applications, 2009
How to choose the best or near kernel function to reduce test error rate is the key of KPCA applied to extract nonlinear feature. In this article, on the basis of research of CA, PSO, we propose a programmer flow of CBPSO used for training kernel function and build CBPSO-KPCA. This approach can effectively optimize kernel function.
Zhao Min, Huixian Yang, Wei Juan, Xunyong Ou   +3 more
openaire   +1 more source

Research on Traffic Prediction Model Based on KPCA

2009
In order to optimize communication network architecture and analysis network capacity, in this paper a traffic prediction model based on kernel function principal component analysis(KPCA) is presented in view of the traffic features. In this model KPCA was used to extract nonlinear features of input data.
Bing-xiang Zhong, Ming-ying You
openaire   +1 more source

PCA and KPCA of ECG signals with binary SVM classification

2011 IEEE Workshop on Signal Processing Systems (SiPS), 2011
Cardiac problems are the main reason of people's death nowadays. However, one way that light save the life is the analysis of the an electrocardiograph. This analysis consist in the diagnosis of the arrhythmia when it presents. In this paper, we propose to combine the Support Vector Machines used in classification on one hand, with the Principal ...
Lara Kanaan, Dalia Merheb, Maya Kallas, Clovis Francis, Hassan Amoud, Paul Honeine   +5 more
openaire   +3 more sources

Stellar Parameterisation using KPCA and SVM

Proceedings of the International Astronomical Union, 2017
AbstractWith so many spectroscopic surveys, both past and upcoming, such as SDSS and LAMOST, the number of accessible stellar spectra is continuously increasing. There is therefore a great need for automated procedures that will derive estimates of stellar parameters. Working with spectra from SDSS and LAMOST, we put forward a hybrid approach of Kernel
H. Yuan, Y. Zhang, Y. Lei, Y. Dong, Z. Bai, G. Li, W. Zhang, H. Zhang, Y. Zhao   +8 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.
openaire   +2 more sources

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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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
openaire   +1 more source