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dc.contributor.author Strange, Harry
dc.contributor.author Zwiggelaar, Reyer
dc.date.accessioned 2010-10-18T14:56:01Z
dc.date.available 2010-10-18T14:56:01Z
dc.date.issued 2010
dc.identifier.citation Strange , H & Zwiggelaar , R 2010 , ' Parallel Projections for Manifold Learning ' . en
dc.identifier.other PURE: 150895
dc.identifier.other dspace: 2160/5809
dc.identifier.uri http://hdl.handle.net/2160/5809
dc.description Harry Strange and Reyer Zwiggelaar. Parallel Projections for Manifold Learning. In Proceedings of the Ninth International Conference on Machine Learning and Applications. Washington DC, December 2010. en
dc.description.abstract Manifold learning is a widely used statistical tool which reduces the dimensionality of a data set while aiming to maintain both local and global properties of the data. We present a novel manifold learning technique which aligns local hyperplanes to build a global representation of the data. A Minimum Spanning Tree provides the skeleton needed to traverse the manifold so that the local hyperplanes can be merged using parallel projections to build a global hyperplane of the data. We show state of the art results when compared against existing manifold learning algorithm on both artificial and real world image data. en
dc.language.iso eng
dc.title Parallel Projections for Manifold Learning en
dc.type Text en
dc.type.publicationtype Conference paper en
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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