Results 1 to 10 of about 192,804 (181)
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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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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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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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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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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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 of Coil Compression in Parallel Imaging. [PDF]
Comput Math Methods Med, 2018 A phased array with many coil elements has been widely used in parallel MRI for imaging acceleration. On the other hand, it results in increased memory usage and large computational costs for reconstructing the missing data from such a large number of channels.
Chang Y, Wang H.
europepmc +5 more sources
Jurnal Ilmu Dasar, 2010 Principal component analysis (PCA) is a method used to reduce dimentionality of the dataset. However, the use of PCA failed to carry out the problem of non-linear and non-separable data.
Ismail Djakaria +2 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, Tommy W. S. Chow
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