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dc.contributor.authorHunt, Thomas
dc.contributor.authorKrener, Arthur J.
dc.date.accessioned2015-04-08T22:08:12Z
dc.date.available2015-04-08T22:08:12Z
dc.date.issued2012-08
dc.identifier.urihttp://hdl.handle.net/10945/44891
dc.description.abstractWe present a new manifold learning algorithm that takes a set of data points lying on or near a lower dimensional manifold as input, possibly with noise, and outputs a simplicial complex that ts the data and the man- ifold. We have implemented the algorithm in the case where the input data has arbitrary dimension, but can be triangulated. We provide triangulations of data sets that fall on the surface of a torus, sphere, swiss roll, and creased sheet embedded in R50. We also discuss the theoretical justi cation of our algorithm.en_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleSimplicial nonlinear principal component analysisen_US
dc.typeArticleen_US
dc.subject.authornonlinear dimensionality reductionen_US
dc.subject.authortangent spaceen_US
dc.subject.authormanifold learningen_US
dc.subject.authorprincipal component analysisen_US
dc.description.funderThis work was supported by NSF DMS-1007399.en_US


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