Iterative Hyperplane Merging: A Framework for Manfiold Learning

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dc.contributor.author Strange, Harry
dc.contributor.author Zwiggelaar, Reyer
dc.date.accessioned 2010-09-02T08:43:31Z
dc.date.available 2010-09-02T08:43:31Z
dc.date.issued 2010-09
dc.identifier.citation Strange , H & Zwiggelaar , R 2010 , ' Iterative Hyperplane Merging: A Framework for Manfiold Learning ' pp. 18 . , 10.5244/C.24.18 en
dc.identifier.other PURE: 150417
dc.identifier.other dspace: 2160/5301
dc.identifier.uri http://hdl.handle.net/2160/5301
dc.description Harry Strange, and Reyer Zwiggelaar. Iterative Hyperplane Merging: A Framework for Manifold Learning. In Frédéric Labrosse, Reyer Zwiggelaar, Yonghuai Liu, and Bernie Tiddeman, editors, Proceedings of the British Machine Vision Conference, pages 18.1-18.11. BMVA Press, September 2010. doi:10.5244/C.24.18. en
dc.description.abstract We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be reconstructed by iteratively merging these hyperplanes. A Minimum Spanning Tree provides the skeleton needed to traverse the manifold so that the local hyperplanes can be used to build a global, locally stable, embedding. We show state of the art results when compared against existing manifold learning approaches using benchmark synthetic data. We also show how our technique can be used on real world image data. en
dc.format.extent 18 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Iterative Hyperplane Merging: A Framework for Manfiold Learning en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.identifier.doi http://dx.doi.org/10.5244/C.24.18
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Vision, Graphics and Visualisation Group en
dc.description.status Non peer reviewed en


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