See without looking: Joint Visualization of sensitive multi-site datasets

Debbrata K. Saha, Vince D. Calhoun, Sandeep R. Panta, Sergey M. Plis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Visualization of high dimensional large-scale datasets via an embedding into a 2D map is a powerful exploration tool for assessing latent structure in the data and detecting outliers. There are many methods developed for this task but most assume that all pairs of samples are available for common computation. Specifically, the distances between all pairs of points need to be directly computable. In contrast, we work with sensitive neuroimaging data, when local sites cannot share their samples and the distances cannot be easily computed across the sites. Yet, the desire is to let all the local data participate in collaborative computation without leaving their respective sites. In this scenario, a quality control tool that visualizes decentralized dataset in its entirety via global aggregation of local computations is especially important as it would allow screening of samples that cannot be evaluated otherwise. This paper introduces an algorithm to solve this problem: decentralized data stochastic neighbor embedding (dSNE). Based on the MNIST dataset we introduce metrics for measuring the embedding quality and use them to compare dSNE to its centralized counterpart. We also apply dSNE to a multi-site neuroimaging dataset with encouraging results.

Original languageEnglish (US)
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
EditorsCarles Sierra
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2672-2678
Number of pages7
ISBN (Electronic)9780999241103
DOIs
StatePublished - 2017
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: Aug 19 2017Aug 25 2017

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume0
ISSN (Print)1045-0823

Other

Other26th International Joint Conference on Artificial Intelligence, IJCAI 2017
CountryAustralia
CityMelbourne
Period8/19/178/25/17

ASJC Scopus subject areas

  • Artificial Intelligence

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