Results 1 to 10 of about 39,250 (155)

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, Irina Novikova, Eugeniy E Mikhailov, Mario A Maldonado, Isaac Fan, Yang Li, Ying-Ju Wang, John Kitching, Andrey B Matsko   +8 more
doaj   +2 more sources

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
doaj   +2 more sources

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
doaj   +3 more sources

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, Yuan Zhongshang, He Yungang, Gao Qingsong, Zhang Bingbing, Xue Fuzhong   +5 more
doaj   +2 more sources

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, Gagan S Sidhu, Nasimeh eAsgarian, Russell eGreiner, Matthew R G Brown   +4 more
doaj   +2 more sources

Kernel Principal Component Analysis for Allen–Cahn Equations

Mathematics
Different researchers have analyzed effective computational methods that maintain the precision of Allen–Cahn (AC) equations and their constant security.
Yusuf Çakır, Murat Uzunca
doaj   +2 more sources

Face recognition using kernel principal component analysis [PDF]

IEEE Signal Processing Letters, 2002
A kernel principal component analysis (PCA) was previously proposed as a nonlinear extension of a PCA. The basic idea is to first map the input space into a feature space via nonlinear mapping and then compute the principal components in that feature space. This article adopts the kernel PCA as a mechanism for extracting facial features.
Kwang In Kim, Keechul Jung, Hang Joon Kim   +2 more
exaly   +3 more sources

Online Kernel Principal Component Analysis: A Reduced-Order Model [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
Kernel principal component analysis (kernel-PCA) is an elegant nonlinear extension of one of the most used data analysis and dimensionality reduction techniques, the principal component analysis. In this paper, we propose an online algorithm for kernel-PCA. To this end, we examine a kernel-based version of Oja's rule, initially put forward to extract a
Paul Honeine
exaly   +4 more sources

Visualization of Iris Data Using Principal Component Analysis and Kernel Principal Component Analysis

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, suryo Guritno, Sri Haryatmi Kartiko   +2 more
doaj   +1 more source

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
openaire   +3 more sources