Compressed Sensing for Big Data Over Complex Networks

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorJung, Alexander
dc.contributor.authorBasirian Jahromi, Saeed
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.supervisorJung, Alexander
dc.date.accessioned2018-01-26T10:53:52Z
dc.date.available2018-01-26T10:53:52Z
dc.date.issued2018-01-22
dc.description.abstractTransductive semi-supervised learning methods aim at automatically labeling large datasets by leveraging information provided by few manually labeled data points and the intrinsic structure of the dataset. Many such methods based on a graph signal representation of a dataset have been proposed, in which the nodes correspond to the data points, the edges connect similar points, and the graph signal is the mapping between the nodes and the labels. Most of the existing methods use deterministic signal models and try to recover the graph signal using a regularized or constrained convex optimization approach, where the regularization/constraint term enforce some sort of smoothness of the graph signal. This thesis takes a different route and investigates a probabilistic graphical modeling approach in which the graph signal is considered a Markov random field defined over the underlying network structure. The measurement process, modeling the initial manually obtained labels, and smoothness assumptions are imposed by a probability distribution defined over the Markov network corresponding to the data graph. Various approximate inference methods such as loopy belief propagation and the mean field methods are studied by means of numerical experiments involving both synthetic and real-world datasets.en
dc.format.extent39 + 7
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/29671
dc.identifier.urnURN:NBN:fi:aalto-201801261175
dc.language.isoenen
dc.programmeMaster’s Programme in Computer, Communication and Information Sciencesfi
dc.programme.majorMachine Learning and Data Miningfi
dc.programme.mcodeSCI3044fi
dc.subject.keywordsemi-supervised learningen
dc.subject.keywordgraph signal learningen
dc.subject.keywordprobabilistic graphical modelsen
dc.subject.keywordapproximate inferenceen
dc.subject.keywordloopy belief propagationen
dc.subject.keywordcomplex networksen
dc.titleCompressed Sensing for Big Data Over Complex Networksen
dc.typeG2 Pro gradu, diplomityöfi
dc.type.ontasotMaster's thesisen
dc.type.ontasotDiplomityöfi
local.aalto.electroniconlyyes
local.aalto.openaccessyes
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