New paradigms for approximate nearest-neighbor search

Ram, Parikshit

Title:

New paradigms for approximate nearest-neighbor search

dc.contributor.advisor	Balcan, Maria-Florina
dc.contributor.advisor	Gray, Alexander G.
dc.contributor.author	Ram, Parikshit
dc.contributor.committeeMember	Lebanon, Guy
dc.contributor.committeeMember	Clarkson, Kenneth L.
dc.contributor.committeeMember	Vempala, Santosh S.
dc.contributor.department	Computational Science and Engineering
dc.date.accessioned	2013-09-20T13:30:03Z
dc.date.available	2013-09-20T13:30:03Z
dc.date.created	2013-08
dc.date.issued	2013-07-02
dc.date.submitted	August 2013
dc.date.updated	2013-09-20T13:30:08Z
dc.description.abstract	Nearest-neighbor search is a very natural and universal problem in computer science. Often times, the problem size necessitates approximation. In this thesis, I present new paradigms for nearest-neighbor search (along with new algorithms and theory in these paradigms) that make nearest-neighbor search more usable and accurate. First, I consider a new notion of search error, the rank error, for an approximate neighbor candidate. Rank error corresponds to the number of possible candidates which are better than the approximate neighbor candidate. I motivate this notion of error and present new efficient algorithms that return approximate neighbors with rank error no more than a user specified amount. Then I focus on approximate search in a scenario where the user does not specify the tolerable search error (error constraint); instead the user specifies the amount of time available for search (time constraint). After differentiating between these two scenarios, I present some simple algorithms for time constrained search with provable performance guarantees. I use this theory to motivate a new space-partitioning data structure, the max-margin tree, for improved search performance in the time constrained setting. Finally, I consider the scenario where we do not require our objects to have an explicit fixed-length representation (vector data). This allows us to search with a large class of objects which include images, documents, graphs, strings, time series and natural language. For nearest-neighbor search in this general setting, I present a provably fast novel exact search algorithm. I also discuss the empirical performance of all the presented algorithms on real data.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/49112
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Similarity search
dc.subject	Nearest-neighbor search
dc.subject	Computational geometry
dc.subject	Algorithms and analysis
dc.subject.lcsh	Nearest neighbor analysis (Statistics)
dc.subject.lcsh	Approximation algorithms
dc.subject.lcsh	Search theory
dc.title	New paradigms for approximate nearest-neighbor search
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.corporatename	College of Computing
local.contributor.corporatename	School of Computational Science and Engineering
relation.isOrgUnitOfPublication	c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isOrgUnitOfPublication	01ab2ef1-c6da-49c9-be98-fbd1d840d2b1
thesis.degree.level	Doctoral