Abstract
Consider an experimental design of a neuroimaging study, where we need to obtain p measurements for each participant in a setting where pʹ(< p) are cheaper and easier to acquire while the remaining (p − pʹ) are expensive. For example, the pʹ measurements may include demographics, cognitive scores or routinely offered imaging scans while the (p − pʹ) measurements may correspond to more expensive types of brain image scans with a higher participant burden. In this scenario, it seems reasonable to seek an “adaptive” design for data acquisition so as to minimize the cost of the study without compromising statistical power. We show how this problem can be solved via harmonic analysis of a band-limited graph whose vertices correspond to participants and our goal is to fully recover a multi-variate signal on the nodes, given the full set of cheaper features and a partial set of more expensive measurements. This is accomplished using an adaptive query strategy derived from probing the properties of the graph in the frequency space. To demonstrate the benefits that this framework can provide, we present experimental evaluations on two independent neuroimaging studies and show that our proposed method can reliably recover the true signal with only partial observations directly yielding substantial financial savings.
Original language | English |
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Title of host publication | Computer Vision - 14th European Conference, ECCV 2016, Proceedings |
Editors | Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling |
Publisher | Springer Verlag |
Pages | 188-205 |
Number of pages | 18 |
ISBN (Print) | 9783319464657 |
DOIs | |
Publication status | Published - 2016 |
Event | 14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands Duration: 2016 Oct 8 → 2016 Oct 16 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9910 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 14th European Conference on Computer Vision, ECCV 2016 |
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Country/Territory | Netherlands |
City | Amsterdam |
Period | 16/10/8 → 16/10/16 |
Bibliographical note
Funding Information:This research was supported by NIH grants AG040396, and NSF CAREER award 1252725, UW ADRC AG033514, UW ICTR 1UL1RR025011, UW CPCP AI117924, UW CIBM 5T15LM007359-14 and Waisman Core Grant P30 HD003352-45.
Publisher Copyright:
© Springer International Publishing AG 2016.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)