Results 181 to 190 of about 3,257 (211)

Gender classification using KPCA and SVM

2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2016
A new technique to construct feature vector for gender classification is proposed in this paper. Here, new feature reduction technique is used to remove the irrelevant features of images. Feature reduction also helps in reducing the over fitting problem of the dataset. KPCA is a kernel based PCA which maps data from original space to non-linear feature
Anjali Goel, Virendra P. Vishwakarma
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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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Face recognition using difference vector plus KPCA

Digital Signal Processing, 2012
In this paper, a novel approach for face recognition based on the difference vector plus kernel PCA is proposed. Difference vector is the difference between the original image and the common vector which is obtained by the images processed by the Gram-Schmidt orthogonalization and represents the common invariant properties of the class.
Ying Wen 0003, Lianghua He, Pengfei Shi
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Facial expression analysis by using KPCA

IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003, 2004
This paper discussed a problem of robustness of existing kernel principal component analysis (KPCA) and proposed a new approach to do facial expression analysis by using KPCA. Experimental results on CMU facial expression image database and Yale database are encouraging.
null Zhong Jin, F. Davoine, null Zhen Lou   +2 more
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Manifold based kernel optimization for KPCA

2011 IEEE 3rd International Conference on Communication Software and Networks, 2011
This paper presents a manifold based kernel optimizing algorithm for KPCA which has recently shown effectiveness for pattern recognition and systematic classification based on extracting nonlinear features. However, their performances largely depend on the kernel function.
Li Zeng, Bin Chen, Linping Du, Kejia Xu
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An application of KPCA in the human face recognition

Proceedings of 2013 2nd International Conference on Measurement, Information and Control, 2013
A face recognition method that based on KPCA and SVM is proposed in this paper. In the method, an SVM support vector machine is employed to process a small-sample-size problem and KPCA kernel principal component analysis is applied to deal with a high order statistics of original data, and to describe the correlation among multiple pixels in an image.
null Meng Qing Song, null Yuan Hai Bo
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Using KPCA feature selection and fusion for intrusion detection

2010 Sixth International Conference on Natural Computation, 2010
The main task of intrusion detection is to extract meaningful and effective features from redundant and noisy features causing poor detection accuracy. A method of feature selection by Kernel Principle Component Analysis (KPCA) and fusion is proposed. The basic features, contend features and traffic features are extracted respectively by KPCA.
Ruixia Zhang, Guojian Zhi
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Hyperspectral Image Recognition Based on KPCA

ICIC Express Letters, Part B: Applications, 2016
Hyperspectral image recognition is an important problem in practical hyperspectral imagery system. While nonlinear problem leads to identification problems, kernel method has provided a promising way to solve it. The performance of kernel-based algorithm is controlled by the appropriateness of kernel function and parameter greatly.
Pan, Jeng-Shyang, Li, Yi-Fan, Li, Jun-Bao, Li, Li, Tsai, Pei-Wei, Su, Qiang, Cui, Wei   +6 more
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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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Automatic Fault Detection and Diagnosis for Sensor Based on KPCA

2009 Second International Symposium on Computational Intelligence and Design, 2009
Automatic Fault detection and diagnosis for sensor is necessary, which affects the performance of the control system seriously. The KPCA effectively captures the nonlinear relationship of the process variables, which computes principal component in high-dimensional feature space by means of integral operators and nonlinear kernel functions.
Yunguang Gao, Shicheng Wang, Zhiguo Liu
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