Pengenalan Jenis Kelamin Berbasis Kernel Principal Component Analysis
Rekayasa, 2015 Gender Recognition adalah salah satu penelitian di bidang biometrik dan computer vision yang cukup popular. Gender Recognition adalah pengembangan dari Face Recognition, Gender Recognition dapat mengklasifikasikan citra menjadi 2 kelas yaitu perempuan ...
Achmad Rizal
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Nonlinear process fault detection and identification using kernel PCA and kernel density estimation [PDF]
, 2016 Kernel principal component analysis (KPCA) is an effective and efficient technique for monitoring nonlinear processes. However, associating it with upper control limits (UCLs) based on the Gaussian distribution can deteriorate its performance.
Cao, Yi, Samuel, Raphael
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Dynamic gesture recognition using PCA with multi-scale theory and HMM [PDF]
, 2001 In this paper, a dynamic gesture recognition system is presented which requires no special hardware other than a Webcam. The system is based on a novel method combining Principal Component Analysis (PCA) with hierarchical multi-scale theory and Discrete ...
Sutherland, Alistair, Wu, Hai
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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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Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces [PDF]
, 2018 In this paper, we study regression problems over a separable Hilbert space with the square loss, covering non-parametric regression over a reproducing kernel Hilbert space.
Cevher, Volkan +3 more
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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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A novel methodology to create generative statistical models of interconnects [PDF]
, 2016 This paper addresses the problem of constructing a generative statistical model for an interconnect starting from a limited set of S-parameter samples, which are obtained by simulating or measuring the interconnect for a few random realizations of its ...
De Geest, Jan +5 more
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Scheduling Dimension Reduction of LPV Models -- A Deep Neural Network Approach [PDF]
, 2020 In this paper, the existing Scheduling Dimension Reduction (SDR) methods for Linear Parameter-Varying (LPV) models are reviewed and a Deep Neural Network (DNN) approach is developed that achieves higher model accuracy under scheduling dimension reduction.
casella +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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