Results 11 to 20 of about 192,804 (181)
Adaptive Process Monitoring of Online Reduced Kernel Principal Component Analysis
Shanghai Jiaotong Daxue xuebao, 2022 In the case of dynamic systems, the traditional kernel principal component analysis (KPCA) method does not perform well. The moving window kernel principal component analysis method can adapt to the normal parameter drift of dynamic systems, but it needs
GUO Jinyu, LI Wentao, LI Yuan
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Thyristor State Evaluation Method Based on Kernel Principal Component Analysis
IEEE Access, 2022 The reliability of the thyristor is directly related to the safe operation of the DC transmission system. A method for evaluating the state of thyristors based on kernel principal component analysis (KPCA) is proposed, which firstly considers the ...
Zhaoyu Lei +6 more
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Comparison Between The Method of Principal Component Analysis And Principal Component Analysis Kernel For Imaging Dimensionality Reduction [PDF]
المجلة العراقية للعلوم الاحصائية, 2019 This paper tackles with two methods to dimensionality reduction, namely principal component analysis (PCA ) in the case of linear combinations and kernel principal component analysis method in the case of nonlinear combinations to digital image ...
Assel Muslim Essa, Asmaa Ghalib Alrawi
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JEDi: java essential dynamics inspector — a molecular trajectory analysis toolkit
BMC Bioinformatics, 2021 Background Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to
Charles C. David +2 more
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Hyperspectral image classification based on spectral-spatial kernel principal component analysis network [PDF]
E3S Web of Conferences, 2020 Hyperspectral imagery contains both spectral information and spatial relationships among pixels. How to combine spatial information with spectral information effectively has always been a research hotspot of hyperspectral image classification.
Fan Yanguo, Hou Shizhe, Yu Dingfeng
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Robust Kernel Principal Component Analysis With ℓ2,1-Regularized Loss Minimization
IEEE Access, 2020 Principal component analysis (PCA) is a widely used unsupervised method for dimensionality reduction. The kernelized version is called kernel principal component analysis (KPCA), which can capture the nonlinear data structure.
Duo Wang, Toshihisa Tanaka
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International Journal of Advanced Robotic Systems, 2020 The multivariate statistical method such as principal component analysis based on linear dimension reduction and kernel principal component analysis based on nonlinear dimension reduction as the modified principal component analysis method are commonly ...
Liming Li +3 more
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Research on Rolling Bearing Fault Diagnosis Based on Volterra Kernel Identification and KPCA
Shock and Vibration, 2023 A rolling bearing fault diagnosis method based on the Volterra series and kernel principal component analysis (KPCA) is proposed. In the proposed method, first, the improved genetic algorithm (IGA) is used to identify the Volterra series model of the ...
Yahui Wang +3 more
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Statistical properties of kernel principal component analysis [PDF]
Machine Learning, 2004 The properties of the eigenvalues of Gram matrices are studied in a non-asymptotic setting. Using local Rademacher averages, we provide data-dependent and tight bounds for their convergence towards eigenvalues of the corresponding kernel operator. We perform these computations in a functional analytic framework which allows to deal implicitly with ...
Blanchard, Gilles +2 more
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Kernel principal component analysis (KPCA) for the de-noising of communication signals [PDF]
, 2002 This paper is concerned with the problem of de-noising for non-linear signals. Principal Component Analysis (PCA) cannot be applied to non-linear signals however it is known that using kernel functions, a non-linear signal can be transformed into a ...
Koutsogiannis, G., Soraghan, J.J.
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