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Algorithm Design for Improving Feature Extraction Efficiency Based on KPCA

Xu Yong,Yangjingyu,Lu Jianfeng

Strategic Study of CAE 2005, Volume 7, Issue 10,   Pages 38-42

Abstract:

KPCA (kernel PCA) is derived from PCA. It can extract nonlinear feature components of samples.However, feature extraction for one sample requires that kernel functions between training samples andAccording to the supposition, an improved KPCA (IKPCA) algorithm is developed.IKPCA extracts feature components of one sample efficiently, only based on kernel functions between nodesExperimental results show that IKPCA is very close to KPCA in performance, while with higher efficiency

Keywords： KPCA(Kernel PCA)     IKPCA(Improved KPCA)     feature extraction     feature space    

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An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 277-291 doi: 10.1007/s11708-021-0731-6

Abstract: surrogate fuels was proposed with the application of a machine learning method, named the Bayesian multiple kernel

Keywords： sooting tendency     yield sooting index     Bayesian multiple kernel learning     surrogate assessment     surrogate    

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Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember

Jing LI,Xiao-run LI,Li-jiao WANG,Liao-ying ZHAO

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 3,   Pages 250-257 doi: 10.1631/FITEE.1500244

Abstract: The kernel new simplex growing algorithm (KNSGA), recently developed as a nonlinear alternative to the

Keywords： Modified Cholesky factorization     Spatial pixel purity index (SPPI)     New simplex growing algorithm (NSGA)     Kernel    

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Development of soft kernel durum wheat

Craig F. MORRIS

Frontiers of Agricultural Science and Engineering 2019, Volume 6, Issue 3,   Pages 273-278 doi: 10.15302/J-FASE-2019259

Abstract:

Kernel texture (grain hardness) is a fundamental and determining factor related to wheat ( spp.) millingThere are three kernel texture classes in wheat: soft and hard hexaploid ( ), and very hard durum ( subspPhenotypically, the easiest means of quantifying kernel texture is with the Single Kernel CharacterizationSoft kernel durum wheat was created via homeologous recombination using the mutation, which facilitatedExpression of the puroindoline genes in durum grain resulted in kernel texture and flour milling characteristics

Keywords： soft durum wheat     grain hardness     puroindolines     milling     baking     pasta     noodles    

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Boundedness of Marcinkiewicz integralwith rough kernel onTriebel-Lizorkin spaces

Chun-jie ZHANG,Fang-fang REN,Yu-huai ZHANG,Gui-lian GAO

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 8,   Pages 654-657 doi: 10.1631/FITEE.1500082

Abstract: Our result shows that the Marcinkiewicz integral, with a bounded radial function in its kernel, is still

Keywords： Marcinkiewicz integral     Triebel-Lizorkin spaces    

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Image quality assessmentmethod based on nonlinear feature extraction in kernel space Article

Yong DING,Nan LI,Yang ZHAO,Kai HUANG

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 10,   Pages 1008-1017 doi: 10.1631/FITEE.1500439

Abstract: Furthermore, by introducing kernel methods to transform the linear problem into a nonlinear one, a full-reference

Keywords： Image quality assessment     Full-reference method     Feature extraction     Kernel space     Support vector regression    

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Frequency-hopping transmitter fingerprint feature recognition with kernel projection and joint representation Research Articles

Ping SUI, Ying GUO, Kun-feng ZHANG, Hong-guang LI

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8,   Pages 1133-1146 doi: 10.1631/FITEE.1800025

Abstract: To address these problems, we propose a novel classifier, called the kernel joint representation classifier(KJRC), for FH transmitter fingerprint feature recognition, by integrating kernel projection, collaborative

Keywords： Frequency-hopping     Fingerprint feature     Kernel function     Joint representation     Transmitter recognition    

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Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation None

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4,   Pages 471-480 doi: 10.1631/FITEE.1620342

Abstract: We propose a fully automatic brain tumor segmentation method based on kernel sparse coding.In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary learningA kernel-clustering algorithm based on dictionary learning is developed to code the voxels.

Keywords： Brain tumor segmentation     Kernel method     Sparse coding     Dictionary learning    

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Potential hybrid feedstock for biodiesel production in the tropics

Solomon GIWA,Oludaisi ADEKOMAYA,Collins NWAOKOCHA

Frontiers in Energy 2016, Volume 10, Issue 3,   Pages 329-336 doi: 10.1007/s11708-016-0408-8

Abstract: The tropics are renowned for abundant oil-bearing crops of which palm kernel oil (PKO) from palm seed

Keywords： groundnut oil     palm kernel oil     methyl ester     fuel properties     tropics     fatty acid composition    

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Seismic fragility curves for structures using non-parametric representations

Chu MAI, Katerina KONAKLI, Bruno SUDRET

Frontiers of Structural and Civil Engineering 2017, Volume 11, Issue 2,   Pages 169-186 doi: 10.1007/s11709-017-0385-y

Abstract: the fragility curves without employing the above assumption, namely binned Monte Carlo simulation and kernel

Keywords： earthquake engineering     fragility curves     lognormal assumption     non-parametric approach     kernel density estimation    

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Indoor carbonyl compounds in an academic building in Beijing, China: concentrations and influencing factors

Chuanjia JIANG, Pengyi ZHANG

Frontiers of Environmental Science & Engineering 2012, Volume 6, Issue 2,   Pages 184-194 doi: 10.1007/s11783-011-0309-3

Abstract: Carbonyl compounds in indoor air are of great concern for their adverse health effects. Between February and May, 2009, concentrations of 13 carbonyl compounds were measured in an academic building in Beijing, China. Total concentration of the detected carbonyls ranged from 20.7 to 189.1 μg·m , and among them acetone and formaldehyde were the most abundant, with mean concentrations of 26.4 and 22.6 μg·m , respectively. Average indoor concentrations of other carbonyls were below 10 μg·m . Principal component analysis identified a combined effect of common indoor carbonyl sources and ventilation on indoor carbonyl levels. Diurnal variations of the carbonyl compounds were investigated in one office room, and carbonyl concentrations tended to be lower in the daytime than at night, due to enhanced ventilation. Average concentrations of carbonyl compounds in the office room were generally higher in early May than in late February, indicating the influence of temperature. Carbonyl source emission rates from both the room and human occupants were estimated during two lectures, based on one-compartment mass balance model. The influence of human occupants on indoor carbonyl concentrations varies with environmental conditions, and may become significant in the case of a large human occupancy.

Keywords： compounds     indoor air     ventilation     human occupancy     source emission rate (SER)     principal component analysis (PCA    

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Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

Frontiers in Energy 2017, Volume 11, Issue 2,   Pages 175-183 doi: 10.1007/s11708-017-0471-9

Abstract: minimal-redundancy-maximal-relevance (mRMR), while the latter does so based on the method of principal component analysis (PCA

Keywords： power forecasting     feature set     minimal-redundancy-maximal-relevance (mRMR)     principal component analysis (PCA    

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Predicting non-carcinogenic hazard quotients of heavy metals in pepper (

Marzieh Mokarram, Hamid Reza Pourghasemi, Huichun Zhang

Frontiers of Environmental Science & Engineering 2020, Volume 14, Issue 6, doi: 10.1007/s11783-020-1331-0

Abstract: . • PCA and random search can select the main spectra and predict THQ for each element.Based on the relevant spectral bands identified by principal component analysis (PCA) and random searchThe results of PCA and random search indicated that the spectra at the bands of b570, b650, and b760

Keywords： Heavy metals     Plants     Target Hazard Quotient (THQ)     Principal Component Analysis (PCA)     Random search     Electromagnetic    

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Non-negativematrix factorization based unmixing for principal component transformed hyperspectral data

Xiu-rui GENG,Lu-yan JI,Kang SUN

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 5,   Pages 403-412 doi: 10.1631/FITEE.1600028

Abstract: Although principal component analysis (PCA) can be used to mitigate these two problems, the transformedIn this paper, we analyze the impact of PCA on NMF, and find that multiplicative NMF can also be applicable

Keywords： Non-negative matrix factorization (NMF)     Principal component analysis (PCA)     Endmember     Hyperspectral    

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A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification

Chuan Choong YANG, Chit Siang SOH, Vooi Voon YAP

Frontiers in Energy 2019, Volume 13, Issue 2,   Pages 386-398 doi: 10.1007/s11708-017-0497-z

Abstract: reduction of the - trajectory plot template images before utilizing the principal component analysis (PCA

Keywords： voltage-current (V-I) trajectory     multi-stage classification algorithm     principal component analysis (PCA    

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Title Author Date Type Operation

Algorithm Design for Improving Feature Extraction Efficiency Based on KPCA

Xu Yong,Yangjingyu,Lu Jianfeng

Journal Article

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Journal Article

Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember

Jing LI,Xiao-run LI,Li-jiao WANG,Liao-ying ZHAO

Journal Article

Development of soft kernel durum wheat

Craig F. MORRIS

Journal Article

Boundedness of Marcinkiewicz integralwith rough kernel onTriebel-Lizorkin spaces

Chun-jie ZHANG,Fang-fang REN,Yu-huai ZHANG,Gui-lian GAO

Journal Article

Image quality assessmentmethod based on nonlinear feature extraction in kernel space

Yong DING,Nan LI,Yang ZHAO,Kai HUANG

Journal Article

Frequency-hopping transmitter fingerprint feature recognition with kernel projection and joint representation

Ping SUI, Ying GUO, Kun-feng ZHANG, Hong-guang LI

Journal Article

Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

Journal Article

Potential hybrid feedstock for biodiesel production in the tropics

Solomon GIWA,Oludaisi ADEKOMAYA,Collins NWAOKOCHA

Journal Article

Seismic fragility curves for structures using non-parametric representations

Chu MAI, Katerina KONAKLI, Bruno SUDRET

Journal Article

Indoor carbonyl compounds in an academic building in Beijing, China: concentrations and influencing factors

Chuanjia JIANG, Pengyi ZHANG

Journal Article

Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

Journal Article

Predicting non-carcinogenic hazard quotients of heavy metals in pepper (

Marzieh Mokarram, Hamid Reza Pourghasemi, Huichun Zhang

Journal Article

Non-negativematrix factorization based unmixing for principal component transformed hyperspectral data

Xiu-rui GENG,Lu-yan JI,Kang SUN

Journal Article

A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification

Chuan Choong YANG, Chit Siang SOH, Vooi Voon YAP

Journal Article