Application of kernel principal component analysis for optical vector atomic magnetometry [PDF]
Machine Learning: Science and Technology, 2023 Vector atomic magnetometers that incorporate electromagnetically induced transparency (EIT) allow for precision measurements of magnetic fields that are sensitive to the directionality of the observed field by virtue of fundamental physics.
James A McKelvy +8 more
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Towards Multiple Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples [PDF]
Journal of Integrative Bioinformatics, 2017 Personalized treatment of patients based on tissue-specific cancer subtypes has strongly increased the efficacy of the chosen therapies. Even though the amount of data measured for cancer patients has increased over the last years, most cancer subtypes ...
Speicher Nora K., Pfeifer Nico
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Nonlinear chemical processes fault detection based on adaptive kernel principal component analysis
Systems Science & Control Engineering, 2020 When kernel Principal Component Analysis (KPCA) is applied to fault detection, kernel Principal Components (KPCs) are divided into two spaces according to the size of variance for fault detection, respectively.
Chen Miao, Zhaomin Lv
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Gene- or region-based association study via kernel principal component analysis [PDF]
BMC Genetics, 2011 Background In genetic association study, especially in GWAS, gene- or region-based methods have been more popular to detect the association between multiple SNPs and diseases (or traits).
Zhao Jinghua +5 more
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Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD [PDF]
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 +4 more
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Kernel Principal Component Analysis for Allen–Cahn Equations
MathematicsDifferent researchers have analyzed effective computational methods that maintain the precision of Allen–Cahn (AC) equations and their constant security.
Yusuf Çakır, Murat Uzunca
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New Insight into Development of Environmental - Economic Model Based on a Composite Environmental Quality Index: An Application of Principal Components Analysis [PDF]
تصمیم گیری و تحقیق در عملیات, 2021 Purpose: This paper aimed to create a composite index for environmental quality. Since the results of empirical studies on economic - environmental variables nexus, considering different environmental indicators, are not consistent with each other; it ...
Hossein Ali Fakher +4 more
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Exactly Robust Kernel Principal Component Analysis [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 Robust principal component analysis (RPCA) can recover low-rank matrices when they are corrupted by sparse noises. In practice, many matrices are, however, of high-rank and hence cannot be recovered by RPCA. We propose a novel method called robust kernel principal component analysis (RKPCA) to decompose a partially corrupted matrix as a sparse matrix ...
Jicong Fan 0001, Tommy W. S. Chow
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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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