Title	Metric denoise: Making it more friendly for topological computation
Creator	Wang, Yusu
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2017-07-31T16:08
Description	Many topological computation tasks, as well as stability results, assume that the input is a "clean" (finite) metric space or with very limited type of noise (e.g, with bounded Hausdorff-type distance). In this talk, I will describe three different ways to model noise in metric, and how to perform denoising so as to produce the more friendly form of Hausdorff-type noise. I will specifically focus on the case when the target metric is induced from a graph; however the observed graph is a (randomly) perturbed version of the true graph. I will also discuss some open problems at the end.
Extent	33 minutes
Subject	Mathematics; Algebraic topology; Probability theory and stochastic processes; Data mining
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Ohio State University
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2018-01-28
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0363275
URI	http://hdl.handle.net/2429/64472
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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