Multi-scale local shape analysis and feature selection in machine learning applications
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© 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.
Published Version (Please cite this version)10.1109/IJCNN.2015.7280428
Publication InfoBendich, P; Gasparovic, E; Harer, J; Izmailov, R; & Ness, L (2015). Multi-scale local shape analysis and feature selection in machine learning applications. Proceedings of the International Joint Conference on Neural Networks, 2015-September. 10.1109/IJCNN.2015.7280428. Retrieved from https://hdl.handle.net/10161/12014.
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Associate Research Professor of Mathematics
I am a mathematician whose main research focus lies in adapting theory from ostensibly pure areas of mathematics, such as topology, geometry, and abstract algebra, into tools that can be broadly used in many data-centeredapplications.My initial training was in a recently-emerging field called topological data analysis (TDA). I have beenresponsible for several essential and widely-used elements of its theoretical toolkit, with a particularfocus on building TDA methodology
Professor of Mathematics
Professor Harer's primary research is in the use of geometric, combinatorial and computational techniques to study a variety of problems in data analysis, shape recognition, image segmentation, tracking, cyber security, ioT, biological networks and gene expression.
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