Results 21 to 30 of about 194,949 (275)
International Journal of Advanced Robotic Systems, 2020 The multivariate statistical method such as principal component analysis based on linear dimension reduction and kernel principal component analysis based on nonlinear dimension reduction as the modified principal component analysis method are commonly ...
Liming Li +3 more
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Research on Rolling Bearing Fault Diagnosis Based on Volterra Kernel Identification and KPCA
Shock and Vibration, 2023 A rolling bearing fault diagnosis method based on the Volterra series and kernel principal component analysis (KPCA) is proposed. In the proposed method, first, the improved genetic algorithm (IGA) is used to identify the Volterra series model of the ...
Yahui Wang +3 more
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Kernel principal component analysis (KPCA) for the de-noising of communication signals [PDF]
, 2002 This paper is concerned with the problem of de-noising for non-linear signals. Principal Component Analysis (PCA) cannot be applied to non-linear signals however it is known that using kernel functions, a non-linear signal can be transformed into a ...
Koutsogiannis, G., Soraghan, J.J.
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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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Streaming Kernel Principal Component Analysis
CoRR, 2015 Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture non-linear structures within large data sets, and is a central tool in data analysis and learning. To allow for non-linear relations, typically a full $n \times n$ kernel matrix is constructed over $n$ data points, but this requires too much space and time ...
Mina Ghashami +2 more
openaire +3 more sources
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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