Results 11 to 20 of about 29,752 (189)
Kernel PCA for multivariate extremes
CoRR, 2022 We propose kernel PCA as a method for analyzing the dependence structure of multivariate extremes and demonstrate that it can be a powerful tool for clustering and dimension reduction. Our work provides some theoretical insight into the preimages obtained by kernel PCA, demonstrating that under certain conditions they can effectively identify clusters ...
Marco Avella-Medina +2 more
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kernlab - An S4 Package for Kernel Methods in R [PDF]
Journal of Statistical Software, 2004 kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 ob ject model and provides a framework for creating and using kernel-based algorithms. The package contains dot product primitives (kernels),
Alexandros Karatzoglou +3 more
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Kernel PCA with the Nyström method [PDF]
CoRR, 2021 47 pages, 6 ...
Hallgren, F
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Speedup of kernel eigenvoice speaker adaptation by embedded kernel PCA [PDF]
Interspeech 2004, 2004 Recently, we proposed an improvement to the eigenvoice (EV) speaker adaptation called kernel eigenvoice (KEV) speaker adaptation. In KEV adaptation, eigenvoices are computed using kernel PCA, and a new speaker’s adapted model is implicitly computed in the kernel-induced feature space.
Brian Mak +2 more
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Semi-Supervised Kernel PCA [PDF]
CoRR, 2010 We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA, penalises within class variances similar to Fisher discriminant analysis. The second, LSKPCA is a hybrid of least squares regression and kernel PCA.
Christian Walder +3 more
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Fast Iterative Kernel PCA [PDF]
, 2007 We introduce two methods to improve convergence of the Kernel Hebbian Algorithm (KHA) for iterative kernel PCA. KHA has a scalar gain parameter which is either held constant or decreased as 1/t, leading to slow convergence.
Nicol N. Schraudolph +2 more
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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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Statistics & Risk Modeling, 2012 Abstract Many statistical estimation techniques for high-dimensional or functional data are based on a preliminary dimension reduction step, which consists in projecting the sample X 1,...,X n onto the first D eigenvectors of the Principal Component Analysis ...
Biau, Gérard, Mas, André
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Fault Diagnosis Method of Wind Turbine Pitch Angle Based on PCA-KNN Fusion Algorithm
Zhongguo dianli, 2021 With regard to the four main fault types of the pitch angle of a wind turbine pitch system and the data analysis of the wind turbine, an identification method of abnormal pitch angles of a wind turbine is proposed, depending on nonparametric kernel ...
Xi CHEN +5 more
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IEEE Access, 2021 As the state-of-the-art technology of Bayesian inference, based on low-dimensional principal components analysis (PCA) subspace inference methods can provide approximately accurate predictive distribution and well calibrated uncertainty.
Yongguang Wang, Shuzhen Yao, Tian Xu
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