KPCAKernel Principal Component Analysis
KPCAKorean Presbyterian Church in America
KPCAKentucky Primary Care Association (Frankfort, KY)
KPCAKings Park Civic Association (Springfield, VA)
KPCAKorean Printed Circuit Association (South Korea)
KPCAKent Primary Care Agency (Kent, England, UK)
Copyright 1988-2018, All rights reserved.
References in periodicals archive ?
However, Mohammad Ibrar Shah, President Hazara Cycling Association, presented a penal of names for various portfolios of the KPCA with the house unanimously given approval by raising their hands.
In a submission to council planners he said: "The VLR is not part of the Adopted Local Plan and contravenes many of the policies in that plan relating to protection, conservation and enhancement of biodiversity, landscapes and the historic environment and importantly the VLR is contrary to the council's own supplementary guidance on Kings Park and the road would increase traffice flow through a Conservation Area and be a dereliction of duty by the council to protect and enhance the KPCA."
Gu, "A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine", Neurocomputing vol.
The procedures of KPCA and PCA are similar, but in KPCA, the kernel function is used instead of the original data [22].
Then, KPCA with RBF was adopted to extract the principal feature of processed spectra.
Furthermore, compared with R feature fusion and KPCA feature fusion will show that the CNMP contains a better feature fusion approach.
To solve problems of KPCA, many extended methods were developed including fast iterative KPCA (FIKPCA) [6], adaptive KPCA [7,8], and multiscale KPCA [9].
Using the modal features, that is, the natural frequencies and COMAC, PCA, KPCA, and ICA are adopted to monitor the dam health.
In the last more than 10 years, many kernel based approaches have been proposed, such as well-known kernel principal component analysis (KPCA) [33, 34] and kernel Fisher discriminant analysis (KFDA) [35].
Kernel Fisher discriminant analysis (KFDA) [10] and Kernel principal component analysis (KPCA) [11] are classical nonlinear feature extraction methods with dimensionality reduction.