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Multi-scale local shape analysis and feature selection in machine learning applications

dc.contributor.author Bendich, P
dc.contributor.author Gasparovic, E
dc.contributor.author Harer, J
dc.contributor.author Izmailov, R
dc.contributor.author Ness, L
dc.date.accessioned 2016-05-13T00:02:00Z
dc.date.issued 2015-09-28
dc.identifier.uri https://hdl.handle.net/10161/12014
dc.description.abstract © 2015 IEEE.We introduce a method called multi-scale local shape analysis for extracting features that describe the local structure of points within a dataset. The method uses both geometric and topological features at multiple levels of granularity to capture diverse types of local information for subsequent machine learning algorithms operating on the dataset. Using synthetic and real dataset examples, we demonstrate significant performance improvement of classification algorithms constructed for these datasets with correspondingly augmented features.
dc.publisher IEEE
dc.relation.ispartof Proceedings of the International Joint Conference on Neural Networks
dc.relation.isversionof 10.1109/IJCNN.2015.7280428
dc.title Multi-scale local shape analysis and feature selection in machine learning applications
dc.type Journal article
duke.contributor.id Bendich, P|0308528
duke.contributor.id Harer, J|0100474
pubs.organisational-group Duke
pubs.organisational-group Mathematics
pubs.organisational-group Trinity College of Arts & Sciences
pubs.publication-status Published
pubs.volume 2015-September


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