Results 11 to 20 of about 194,949 (275)
Kernel principal component analysis of the ear morphology [PDF]
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017 This paper describes features in the ear shape that change across a population of ears and explores the corresponding changes in ear acoustics. The statistical analysis conducted over the space of ear shapes uses a kernel principal component analysis (KPCA).
Reza Zolfaghari +4 more
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Two-Phase Incremental Kernel PCA for Learning Massive or Online Datasets [PDF]
Complexity, 2019 As a powerful nonlinear feature extractor, kernel principal component analysis (KPCA) has been widely adopted in many machine learning applications. However, KPCA is usually performed in a batch mode, leading to some potential problems when handling ...
Feng Zhao +5 more
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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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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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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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HYPERPARAMETER SELECTION IN KERNEL PRINCIPAL COMPONENT ANALYSIS [PDF]
Journal of Computer Science, 2014 In kernel methods, choosing a suitable kernel is in dispensable for favorable results. No well-founded methods, however, have been established in general for unsupervised learning. We focus on kernel Princ ipal Component Analysis (kernel PCA), which is a nonlinear extension of principal component analysis and ha s been used electively for extracting ...
Md. Ashad Alam, Kenji Fukumizu
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