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The kernel pca algorithms for wide data .1. Theory and algorithms

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 1997年第2期36卷 165-172页

作者： Wu, W Massart, DL deJong, S FREE UNIV BRUSSELS INST PHARMACEUTCHEMOACB-1090 BRUSSELSBELGIUM UNILEVER RES LABS VLAARDINGEN NL-3133 AT VLAARDINGENNETHERLANDS

Four classic pca algorithms: NIPALS, the power method (POWER), singular value decomposition (SVD) and eigenvalue decomposition (EVD) are modified into their kernel version to analyse wide data sets. For such data sets with many variables and fewer objects, the classic algorithms become very inefficient, because the size of the associated matrix X'.X (pXp) is very large. Based on the kemel matrix X.X' (nXn), the kernel algorithms are developed. They yield the same principal components but, when the number of variables is higher than the number of objects, they are faster than the corresponding classic algorithms. Simulation results confirm this property and also show that the kernel EVD is the most efficient algorithm for wide data sets, and that the difference between the kernel SVD and EVD is not very large. Therefore, it is recommended to use the kernel EVD for wide data sets whether all PCs or only the first few are required. A Matlab pca program which efficiently deals with all kinds of data sets was developed.

关键词： pca algorithm kernel wide data

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Research and Implementation of pca Face Recognition algorithm Based on Matlab

Research and Implementation of PCA Face Recognition Algorith...

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International Conference on Engineering Technology and Application (ICETA)

作者： Fu, Qi Shandong Agr & Engn Univ Jinan Shandong Peoples R China

This paper researches the theory of pca (Principle Component Analysis) algorithm and the feature extraction elements in the process of face recognition, summarizes application procedures of pca algorithm in the process face recognition, and realizes the application of pca algorithm in the process face recognition in the matlab software. The research content and realization results show that: pca algorithm is a kind of algorithm which is very suitable for programming and realization of matlab software;the key factor to realize pca algorithm is the selection of the number of feature vectors, which affects the recognition rate and training time of the space sample subset. The higher recognition rate indicates better results in the algorithm implementation;the shorter training time of the space sample subset indicates more excellent algorithm implementation. In the process of selection of the number of feature vectors, on one hand, there is a need to protect the recognition rate;on the other hand, there is a need to control training time of the space sample subset, in which the recognition rate is a rigid target. The shortest training time of the subset of samples is selected on the premise of meeting the recognition rate.

关键词： pca algorithm face recognition training time recognition rate matlab realization

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Comparison of LLE and pca algorithms for Gene Expression Data Analysis

Comparison of LLE and PCA Algorithms for Gene Expression Dat...

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International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Chen, Xiaozhou Yang, Fan Li, Huamei Chen, Junhua Yunnan Univ Nationalities Sch Math & Comp Sci Kunming 650031 Peoples R China

ISBN: (纸本)9783038350125

According to the problem that the linear dimension reduction is not effective to understand gene expression data. using the manifold learning as a guide, analysing dimensionality reduction of gene expression data, selecting colon cancer and leukaemia gene expression datasets for investigation, using inter category distances as the criteria to quantitatively evaluate the effects of data dimensionality reduction. Experiments show that LLE algorithm is more suitable method for the gene expression data. The LLE analyses indicate that there is a clear distinction boundary between the healthy people and the cancer patients.

关键词： LLE algorithm pca algorithm dimensional reduction gene expression Data

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Research and Implementation of pca Face Recognition algorithm Based on Matlab

Research and Implementation of PCA Face Recognition Algorith...

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2015 2nd International Conference on Engineering Technology and Application

作者： Qi Fu Shandong Agriculture and Engineering University

This paper researches the theory of pca(Principle Component Analysis) algorithm and the feature extraction elements in the process of face recognition,summarizes application procedures of pca algorithm in the process face recognition,and realizes the application of pca algorithm in the process face recognition in the matlab *** research content and realization results show that:pca algorithm is a kind of algorithm which is very suitable for programming and realization of matlab software;the key factor to realize pca algorithm is the selection of the number of feature vectors,which affects the recognition rate and training time of the space sample *** higher recognition rate indicates better results in the algorithm implementation;the shorter training time of the space sample subset indicates more excellent algorithm *** the process of selection of the number of feature vectors,on one hand,there is a need to protect the recognition rate;on the other hand,there is a need to control training time of the space sample subset,in which the recognition rate is a rigid *** shortest training time of the subset of samples is selected on the premise of meeting the recognition rate.

关键词： pca algorithm face recognition training time recognition rate matlab realization

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Research Based on Improving pca Face Recognition 4

Research Based on Improving PCA Face Recognition

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4th International Conference on Intelligent Information Processing (ICIIP)

作者： Xu Henghui Sun Xuan Xiao Wenwen Guilin Univ Technol Coll Mech & Control Engn Guilin 541004 Peoples R China

ISBN: (纸本)9781450361910

The Principal Component Analysis (pca) algorithm is widely used in the field of face recognition because of its high recognition rate and simplicity. The pca algorithm is based on the principle of Karhunen-Loeve Transformation, because the pca algorithm is sensitive to outliers, it is improved on the basis of pca algorithm, combined with Linear Discriminant Analysis (LDA) algorithm, the pca-LDA face recognition method is proposed. This method obtains the feature space of training sample set by pca algorithm, On this basis, the LDA algorithm is executed to obtain the feature space of fusion. The pca is then fused with the LDA's feature space to obtain the new feature space that combines the two. Finally, the face projected in the new feature space is trained and recognized. The experimental results show that the face recognition algorithm proposed in this paper has a higher recognition rate than the traditional pca algorithm.

关键词： Face recognition pca algorithm LDA algorithm Feature space

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Improved pca-BP Face Recognition Based on Construction of Virtual Sample 17th

Improved PCA-BP Face Recognition Based on Construction of Vi...

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17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Lin, Jing Wu, Xisheng Yan, Xudong Jiangnan Univ Coll Internet Things Engn Wuxi 214122 Peoples R China

ISBN: (纸本)9783319462578;9783319462561

According to the lack of training samples and the shortage of the traditional pca and BP algorithm, this paper proposes an improved pca-BP face recognition algorithm based on the construction of virtual samples. Firstly, the algorithm uses original training samples to generate virtual samples by mirror and rotation transformation, and then all of the original samples and virtual samples are used for the improved pca algorithm to extract the feature vectors. Moreover, the extracted feature vectors are input to the improved BP neural network for training, and finally combined K-Nearest Neighbor algorithm comprehensive discriminant classification, the generalization ability of face image recognition is enhanced. The experimental result on ORL, YALE, FERET and AR face database proves that the proposed algorithm can predict the possible changes of the samples in a certain extent, and the recognition rate is improved obviously.

关键词： Virtual samples pca algorithm BP neural network K-Nearest neighbor algorithm Face recognition

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Overview of principal component analysis algorithm

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OPTIK 2016年第9期127卷 3935-3944页

作者： Li, Lingjun Liu, Shigang Peng, Yali Sun, Zengguo Minist Educ Key Lab Modern Teaching Technol Xian 710062 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

Principal component analysis (pca) algorithm has been extensively employed in face recognition. However, existing pca algorithms have some limitations in face recognition. In order to overcome these limitations, researchers proposed some extended-pca algorithms. In this paper, we present algorithm implementation of the original pca algorithm and main extended-pca algorithms including two-dimensional pca (2Dpca), 2Dpca-based feature fusion approach, the kernel pca (Kpca), the modular pca, improved,Kpca (IKpca), efficient sparse Kpca (ESKpca) and incremental pca (Ipca). 2Dpca directly computes the projection of a matrix onto a transforming axis. 2Dpca-based feature fusion approach combines the features generated from the two schemes of 2Dpca. Kpca can perform well in extracting features from samples whose components have nonlinear relations. The modular pca approach divides the original face image into sub-images and applies the original pca approach to each of these sub images. The IKpca algorithm improves Kpca for more efficient feature extraction. The efficient sparse Kpca (ESKpca) improves the computational efficiency of the previous sparse Kpca methods on large-scale training sample sets. Incremental pca (Ipca) overcomes the limitation which is hard to scale up the developed systems. In order to compare the recognition rate of these algorithms in face recognition, a series of experiments are performed on three face image databases: ORL, Yale and NIR face databases. (C) 2016 Elsevier GmbH. All rights reserved.

关键词： Face recognition pca algorithm Extended-pca algorithms Recognition rate

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Human motion recognition using an eigenspace

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PATTERN RECOGNITION LETTERS 2005年第6期26卷 687-697页

作者： Rahman, MM Ishikawa, S Kyushu Inst Technol Dept Control Engn Kitakyushu Fukuoka 8048550 Japan

This paper describes a method for representing and/or recognizing human motion using an eigenspace analysis, referred to a tuned eigenspace, that is responsible for storing various human motions in terms of their sequential postures in an eigenspace and for recognizing unfamiliar posture and/or motion from the tuned eigenspace. We have also analyzed human dress texture problem, caused by wearing clothes, in the proposed method. The performed experiments include representation and recognition of (i) 6 actions of a cricket umpire played by 22 persons wearing respective dresses, and (ii) a turning motion given by a particular person wearing 10 typical clothes. The experimental results show a satisfactory performance of the proposed method for representing and recognizing human posture and/or motion overcoming the dress problem. (c) 2004 Elsevier B.V. All rights reserved.

关键词： motion analysis eigenspace dress problem pca algorithm tuned eigenspace

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Dimension reduction of image deep feature using pca

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2019年 63卷 102578-000页

作者： Ma, Ji Yuan, Yuyu Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Software Beijing Peoples R China

Convolution neural networks based methods can derive deep features from training images. However, one challenge is that the dimension of the extracted image features increases dramatically with more network layers. To solve this problem, this paper focuses on the study of dimension reduction. After using deep learning to extract image features, the pca algorithm is used to achieve dimension reduction. Specifically, we first leverage deep convolutional neural network to extract image features. Then, we introduce and leverage pca algorithm to achieve dimension reduction. Aiming at the problem that it is difficult to process high-dimensional sparse big data based on pca algorithm. This paper optimizes the pca algorithm. After image preprocessing, the feasibility of pca algorithm for dimension reduction of image feature extraction by deep learning is verified by simulation experiments. The efficiency of the proposed algorithm is proved by comparing the performance of pca algorithm before and after optimization. (C) 2019 Published by Elsevier Inc.

关键词： Deep learning Feature extraction Dimension reduction pca algorithm

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Face Recognition System using Improved Artificial Bee Colony algorithm

Face Recognition System using Improved Artificial Bee Colony...

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International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

作者： Khan, Neha Gupta, Manish VITM Dept CSE Gwalior India

ISBN: (纸本)9781467399395

In this paper, Face recognition system using improved artificial bee colony algorithm is proposed. This system works on two different phases, named as Extraction phase and Recognition phase. In the extraction phase, sub-window extraction algorithm is applied on different face images collected from different sources. Then applied artificial bee colony algorithm to reduce number of sub windows, taken from sub window extraction algorithm. This proposed work helps to improve the overhead problem of pca algorithm. Experiments are carried out on different datasets, collected from FACE_94, and VITM datasets. Experimental results show that proposed work efficiency, in terms of recognition rate, is better than original pca algorithm by reducing number of sub-windows.

关键词： Artificial bee colony ABC Sub-window extraction pca algorithm pca Face Recognition

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