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dc.contributor.authorXie, Shengkun
dc.contributor.authorLawniczak, Anna T.
dc.contributor.authorKrishnan, Sridhar
dc.contributor.authorLio, Pietro
dc.date.accessioned2017-10-03T07:42:16Z
dc.date.available2017-10-03T07:42:16Z
dc.date.issued2012-7-29
dc.identifier.citationShengkun Xie, Anna T. Lawniczak, Sridhar Krishnan, and Pietro Lio, “Wavelet Kernel Principal Component Analysis in Noisy Multiscale Data Classification,” ISRN Computational Mathematics, vol. 2012, Article ID 197352, 13 pages, 2012. doi:10.5402/2012/197352
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/267580
dc.description.abstractWe introduce multiscale wavelet kernels to kernel principal component analysis (KPCA) to narrow down the search of parameters required in the calculation of a kernel matrix. This new methodology incorporates multiscale methods into KPCA for transforming multiscale data. In order to illustrate application of our proposed method and to investigate the robustness of the wavelet kernel in KPCA under different levels of the signal to noise ratio and different types of wavelet kernel, we study a set of two-class clustered simulation data. We show that WKPCA is an effective feature extraction method for transforming a variety of multidimensional clustered data into data with a higher level of linearity among the data attributes. That brings an improvement in the accuracy of simple linear classifiers. Based on the analysis of the simulation data sets, we observe that multiscale translation invariant wavelet kernels for KPCA has an enhanced performance in feature extraction. The application of the proposed method to real data is also addressed.
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.titleWavelet Kernel Principal Component Analysis in Noisy Multiscale Data Classification
dc.typeArticle
dc.date.updated2017-07-13T08:39:20Z
dc.description.versionPeer Reviewed
dc.language.rfc3066en
dc.rights.holderCopyright © 2012 Shengkun Xie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.identifier.doi10.17863/CAM.13519
rioxxterms.versionofrecord10.5402/2012/197352


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