Learning to adapt under practical sensing constraints

Massimino, Andrew K.

Title:

Learning to adapt under practical sensing constraints

dc.contributor.advisor	Davenport, Mark A.
dc.contributor.author	Massimino, Andrew K.
dc.contributor.committeeMember	Romberg, Justin
dc.contributor.committeeMember	Rozell, Christopher J.
dc.contributor.committeeMember	Xie, Yao
dc.contributor.committeeMember	Bloch, Matthieu R.
dc.contributor.department	Electrical and Computer Engineering
dc.date.accessioned	2019-01-16T17:24:06Z
dc.date.available	2019-01-16T17:24:06Z
dc.date.created	2018-12
dc.date.issued	2018-11-09
dc.date.submitted	December 2018
dc.date.updated	2019-01-16T17:24:07Z
dc.description.abstract	The purpose of this work is to explore the capability of sensing systems to acquire information adaptively when they are subject to practical measurement constraints. By leveraging problem structure such as sparsity and probabilistic data models, intelligent sampling schemes have the potential to enable higher quality estimation with less sensing effort in diverse applications such as imaging, recommendation systems, information retrieval, and psychometric studies. Existing approaches to adaptive sensing are often limited in practice as they require the ability to take arbitrary measurements while in realistic situations, measurements must taken according to various limitations. Two representative constrained scenarios are considered: linear settings in which measurement rows are chosen from a fixed collection and where estimation may be performed only via sequentially chosen paired comparisons. Theoretical and empirical evidence are provided to suggest that adaptivity can result in substantial improvements in these constrained settings.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/60776
dc.publisher	Georgia Institute of Technology
dc.subject	Sparsity
dc.subject	Compressive sensing
dc.subject	Adaptive sensing
dc.subject	Constrained sensing
dc.subject	Optimal experimental design
dc.subject	Pairwise comparisons
dc.subject	Recommender systems
dc.subject	Preferences
dc.title	Learning to adapt under practical sensing constraints
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Davenport, Mark A.
local.contributor.corporatename	School of Electrical and Computer Engineering
local.contributor.corporatename	College of Engineering
relation.isAdvisorOfPublication	1162b098-768c-4269-839c-db771101c01b
relation.isOrgUnitOfPublication	5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level	Doctoral