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dc.contributor.authorYang, Haixuan
dc.date.accessioned2014-01-23T09:56:04Z
dc.date.available2014-01-23T09:56:04Z
dc.date.issued2005
dc.identifier.citationYang, Haixuan and King, Irwin and Lyu, Michael R (2005) NHDC and PHDC: Non-propagating and propagating heat diffusion classifiers Proceedings of 12th International Conference on Neural Information Processingen_US
dc.identifier.urihttp://hdl.handle.net/10379/4002
dc.descriptionConference paperen_US
dc.description.abstractAbstract - By imitating the way that heat flows in a medium with a geometric structure, we propose two novel classification algorithms, Non-propagating Heat Diffusion Classifier (NHDC) and Propagating Heat Diffusion Classifier (PHDC). In NHDC, an unlabelled data is classified into the class that diffuses the most heat to the unlabelled data after one local diffusion from time 0 to a small time period, while in PHDC, an unlabelled data is classified into the class that diffuses the most heat to the unlabelled data in the propagating effect of the heat flow from time 0 to time t, which means that in PHDC, the heat diffuses infinitely many times from time 0 and each time period is infinitely small. In other words, we measure the similarity between an unlabelled data and a class by the heat amount that the unlabelled data receives from the set of labelled data in the class, and then classify the unlabelled data into the class with the most similarity. Unlike the traditional method, in which the heat kernel is applied to a kernel-based classifier we employ the heat kernel to construct the classifier directly; moreover, instead of imitating the way that the heat flows along a linear or nonlinear manifold, we let the heat flow along a graph formed by the k-nearest neighbors. An important and special feature in both NHDC and PHDC is that the kernel is not symmetric. We show theoretically that PWA (Parzen Window Approach when the window function is a multivariate normal kernel) and KNN are actually special cases of NHDC model, and that PHDC has the ability to approximate NHDC. Experiments show that NHDC performs better than PWA and KNN in prediction accuracy, and that PHDC performs better than NHDC. I.en_US
dc.formatapplication/pdfen_US
dc.language.isoenen_US
dc.publisher12th International Conference on Neural Information Processingen_US
dc.relation.ispartofProceedings of 12th International Conference on Neural Information Processingen
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectNon-propagating Heat Diffusion Classifier (NDHC)en_US
dc.subjectPropagating Heat Diffusion Classifier (PHDC)en_US
dc.subjectHeat diffusionen_US
dc.titleNHDC and PHDC: Non-propagating and propagating heat diffusion classifiersen_US
dc.typeConference Paperen_US
dc.date.updated2013-09-25T12:49:53Z
dc.local.publishedsourcehttp://www.cse.cuhk.edu.hk/~lyu/paper_pdf/YangKL2005.pdfen_US
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funder|~|
dc.internal.rssid3760891
dc.local.contactHaixuan Yang, School Of Mathematics,Statistics, & Applied Mathematics, Adb-G013, Nui Galway. 2320 Email: haixuan.yang@nuigalway.ie
dc.local.copyrightcheckedNo
dc.local.versionACCEPTED
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