Results 151 to 160 of about 7,354 (203)

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

Face Recognition Using KPCA and KFDA

Applied Mechanics and Materials, 2013
KPCA extracting principal component with nonlinear method is an improved PCA. The KPCA can extract the feature set which is more suitable in categorization than the conventional PCA. The method of KFDA is equivalent to KPCA plus LDA. KPCA is first performed and then LDA is used for a second feature extraction in the KPCA-transformed space. The KPCA and
Hong Mei Li, Dong Ming Zhou, Ren Can Nie, Xiang Li, Hai Ying Deng   +4 more
openaire   +1 more source

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
openaire   +1 more source

Image classification with parallel KPCA‐PCA network

Computational Intelligence, 2022
AbstractPrincipal component analysis (PCA) is widely used in computer vision for object detection. In this article, we take advantage of the algorithms of PCA and kernel principal component analysis (KPCA) to construct a deep learning model named parallel KPCA‐PCA network (PK‐PCANet).
Feng Yang, Zheng Ma, Mei Xie
openaire   +1 more source

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
openaire   +1 more source

Adaptive KPCA Modeling of Nonlinear Systems

IEEE Transactions on Signal Processing, 2015
This paper proposes an adaptive algorithm for kernel principal component analysis (KPCA). Compared to existing work: i) the proposed algorithm does not rely on assumptions, ii) combines the up- and downdating step to become a single operation, iii) the adaptation of the eigendecompsition can, computationally, reduce to $O(N)$ and iv) the proposed ...
Zhe Li, Uwe Kruger, Lei Xie, Ali Almansoori, Hongye Su   +4 more
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

Learning KPCA for Face Recognition

2013
Kernel principal component analysis (KPCA) is an effective method for face recognition. However, the expression of its final solution needs to take advantage of all training examples, such that its run in real-world application with large scale training set is time-consuming.
Wangli Hao, Jianwu Li, Xiao Zhang
openaire   +1 more source

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, Lianghua He, Pengfei Shi
openaire   +1 more source