Results 31 to 40 of about 26,356,550 (281)
Supervised Kernel Principal Component Analysis by Most Expressive Feature Reordering
Journal of Telecommunications and Information Technology, 2015 The presented paper is concerned with feature space derivation through feature selection. The selection is performed on results of kernel Principal Component Analysis (kPCA) of input data samples.
Krzysztof Ślot +3 more
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Face Recognition Based on Robust Principal Component Analysis and Kernel Sparse Representation [PDF]
Jisuanji gongcheng, 2016 Aiming at the problems that the existing face recognition methods are hard to efficiently overcome the effect of noise and error disturbance (such as illumination,occlusion,and face expression).Kernel sparse representation classification based on Robust ...
LIAO Ruihua,LI Yongfan,LIU Hong
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Weighted SNP set analysis in genome-wide association study. [PDF]
PLoS ONE, 2013 Genome-wide association studies (GWAS) are popular for identifying genetic variants which are associated with disease risk. Many approaches have been proposed to test multiple single nucleotide polymorphisms (SNPs) in a region simultaneously which ...
Hui Dai +9 more
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Fault Localization for Synchrophasor Data using Kernel Principal Component Analysis
Advances in Electrical and Computer Engineering, 2017 In this paper, based on Kernel Principal Component Analysis (KPCA) of Phasor Measurement Units (PMU) data, a nonlinear method is proposed for fault location in complex power systems.
CHEN, R., SUN, X., LIU, G.
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Submarine Threat Degree Assessment Model Based on Hybrid Kernel Principal Component Analysis [PDF]
Jisuanji gongcheng, 2018 Target threat degree assessment is a crucial link in submarine operations.In order to reduce the complexity of the evaluation and improve the accuracy of evaluation,according to the diversity of the target space sources,the submarine threat degree ...
DONG Xue,ZHANG Deping
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Energies, 2022 Aiming at the typical faults in the coal mills operation process, the kernel extreme learning machine diagnosis model based on variational model feature extraction and kernel principal component analysis is offered.
Hui Zhang +9 more
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EURASIP Journal on Advances in Signal Processing, 2009 Kernel principal component analysis (KPCA) is investigated for feature extraction from hyperspectral remote sensing data. Features extracted using KPCA are classified using linear support vector machines.
Mathieu Fauvel +2 more
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Journal of Robotics, 2021 In order to better realize the orchard intelligent mechanization and reduce the labour intensity of workers, the study of intelligent fruit boxes handling robot is necessary.
Xinning Li +4 more
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Remote Sensing, 2019 Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPCA ...
Lan Zhang, Hongjun Su, Jingwei Shen
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Communication Efficient Distributed Kernel Principal Component Analysis [PDF]
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016 Kernel Principal Component Analysis (KPCA) is a key machine learning algorithm for extracting nonlinear features from data. In the presence of a large volume of high dimensional data collected in a distributed fashion, it becomes very costly to communicate all of this data to a single data center and then perform kernel PCA.
Maria-Florina Balcan +4 more
openaire +3 more sources