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, Paul L; Gasparovic, E; Harer, John; 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 http://hdl.handle.net/10161/12014.
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Associate Research Professor of Mathematics
I work in computational topology, which for me means adapting and using tools from algebraic topology in order to study noisy and high-dimensional datasets arising from a variety of scientific applications. My thesis research involved the analysis of datasets for which the number of degrees of freedom varies across the parameter space. The main tools are local homology and intersection homology, suitably redefined in this fuzzy multi-scale context. I am also working on building connections bet
Assist Research Professor in the Department of Mathematics
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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