Title	Persistent homology: an approach for high dimensional data analysis
Creator	Heo, Giseon
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2016-02-05T09:04
Description	Topological data analysis (TDA) has been popularized since its development in early 2000. TDA has shown its effectiveness in discerning true features from noise in high-dimensional data. In this talk, we will introduce persistent homology, a particular branch of computational topology and discuss how it can be incorporated to classical statistics and techniques in machine learning. We will demonstrate its usefulness in classifying ADHD subjects. This is a joint project with Rui Hu, Zhichun Zhai, Linglong Kong and Bei Jiang.
Extent	33 minutes
Subject	Mathematics; Statistics; Game theory, economics, social and behavioral sciences
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: University of Alberta
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2016-08-06
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0307401
URI	http://hdl.handle.net/2429/58702
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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