Results 81 to 90 of about 3,257 (211)

On the Use of KPCA to Extract Artifacts in One-Dimensional Biomedical Signals [PDF]

, 2006
Kernel principal component analysis(KPCA) is a nonlinear projective technique that can be applied to decompose multi-dimensional signals and extract informative features as well as reduce any noise contributions.
Lang, E., Schachtner, R., Stadlthanner, K., E. Lang, A. Teixeira, A. Tome, Teixeira, Ana, R. Schachtner, Tome, A., K. Stadlthanner   +9 more
core   +1 more source

Fault Detection for Aircraft Turbofan Engine Using a Modified Moving Window KPCA

IEEE Access, 2020
As a typical data-driven fault detection approach, the moving window kernel principal component analysis (MWKPCA) method has attracted attention for fault detection of turbofan engines considering the presence of component degradation, but the ...
Hao Sun, Yingqing Guo, Wanli Zhao
doaj   +1 more source

Mixed KPCA结合纹理特征的SVM盐碱土信息提取/Method of salt-affected soil information extraction based on Support Vector Machine with Mixed KPCA and texture features[J]

, 2012
核函数是核主成分分析(Kernel Principal Component Analysis,KPCA)的核心,目前使用的核函数都是单一核函数.尝试通过将光谱角径向基核函数(Spectral Angle Radial Basis Function,SA-RBF)与RBF组合形成混合核函数.在研究中,利用基于该混合核函数的KPCA进行特征提取,将其光谱特征波段和纹理特征相结合用于盐碱土的SVM分类,将分类结果与其他SVM分类进行比较,结果表明:该方法优于其他SVM方法 ...
中国科学院研究生院,北京100049, 罗毅, 李春轩, CUI Linlin, LI Chunxuan, 中国科学院新疆生态与地理研究所,乌鲁木齐830011, LUO Yi, 包安明, BAO Anming, 崔林林, 中国科学院新疆生态与地理研究所,乌鲁木齐,830011   +10 more
core  

Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD

Frontiers in Systems Neuroscience, 2012
This article explores various preprocessing tools that select/create features to help a learner produce a classifier that can use fMRI data to effectively discriminate Attention-Deficit Hyperactivity Disorder (ADHD) patients from healthy controls.
Gagan S Sidhu, Gagan S Sidhu, Nasimeh eAsgarian, Russell eGreiner, Matthew R G Brown   +4 more
doaj   +1 more source

Integrating KPCA with an improved evolutionary algorithm for knowledge discovery in fault diagnosis

, 2000
In this paper, a novel approach to knowledge discovery is proposed based on the integration of kernel principal component analysis (KPCA) with an improved evolutionary algorithm.
Qu, Liangsheng, Sun, Ruixiang, Tsung, Fugee   +2 more
core   +1 more source

Regularized kernel function parameter of KPCA using WPSO-FDA for feature extraction and fault recognition of gearbox [PDF]

, 2018
Gearbox is subject to damage or malfunctions by complicated factors such as installation position and operation condition, meanwhile, accompanied by some nonlinear behaviors, which increase the difficulty of fault diagnosis and identification.
Zongyan Wang, Yan He
core   +2 more sources

Neutron/gamma (n/γ) discrimination method based on KPCA-MPA-ELM

He jishu
BackgroundNeutrons/Gamma (n/γ) discrimination is critical for neutron detection in the presence of γ radiation and traditional pulse shape discrimination methods suffer from unstable discrimination accuracy.PurposeThis study aims to implement a machine ...
HU Wanping, ZHANG Guiyu, ZHANG Yunlong, TUO Xianguo, LI Hulin   +4 more
doaj   +1 more source

A KPCA texture feature model for efficient segmentation of RADARSAT-2 SAR sea ice imagery

, 2014
Sea ice information obtained from synthetic aperture radar (SAR) images is crucial for ensuring safe marine navigation and supporting climate change studies in polar regions.
李军, Wong, Alexander, Wang, Cheng, Xu, Linlin, Li, Jonathan, 王程   +5 more
core  

A New Feature Extraction Method Based on the Information Fusion of Entropy Matrix and Covariance Matrix and Its Application in Face Recognition

Entropy, 2015
The classic principal components analysis (PCA), kernel PCA (KPCA) and linear discriminant analysis (LDA) feature extraction methods evaluate the importance of components according to their covariance contribution, not considering the entropy ...
Shunfang Wang, Ping Liu
doaj   +1 more source

Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas

EURASIP Journal on Advances in Signal Processing, 2009
Kernel principal component analysis (KPCA) is investigated for feature extraction from hyperspectral remote sensing data. Features extracted using KPCA are classified using linear support vector machines.
Mathieu Fauvel, Jocelyn Chanussot, Jón Atli Benediktsson   +2 more
doaj   +1 more source