Random Diffusion Representations

Moshe Salhov, Amir Averbuch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The diffusion maps is a kernel based method for manifold learning and data analysis that models a Markovian process over data. Analysis of this process provides meaningful information about the inner geometric structures in the data.In this paper, we present a representation framework for analyzing datasets. This framework is based on a random approximation of the diffusion maps kernel. The resulted representation approximate the pair-wise diffusion distance, does not depend on the data size and it is invariant to scale.

Original languageEnglish
Title of host publication2019 13th International Conference on Sampling Theory and Applications, SampTA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728137414
DOIs
StatePublished - Jul 2019
Event13th International Conference on Sampling Theory and Applications, SampTA 2019 - Bordeaux, France
Duration: 8 Jul 201912 Jul 2019

Publication series

Name2019 13th International Conference on Sampling Theory and Applications, SampTA 2019

Conference

Conference13th International Conference on Sampling Theory and Applications, SampTA 2019
Country/TerritoryFrance
CityBordeaux
Period8/07/1912/07/19

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