Compressed Sensing for Big Data Over Complex Networks

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Jung, Alexander
dc.contributor.author Basirian Jahromi, Saeed
dc.date.accessioned 2018-01-26T10:53:52Z
dc.date.available 2018-01-26T10:53:52Z
dc.date.issued 2018-01-22
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/29671
dc.description.abstract Transductive 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.extent 39 + 7
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Compressed Sensing for Big Data Over Complex Networks en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword semi-supervised learning en
dc.subject.keyword graph signal learning en
dc.subject.keyword probabilistic graphical models en
dc.subject.keyword approximate inference en
dc.subject.keyword loopy belief propagation en
dc.subject.keyword complex networks en
dc.identifier.urn URN:NBN:fi:aalto-201801261175
dc.programme.major Machine Learning and Data Mining fi
dc.programme.mcode SCI3044 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Jung, Alexander
dc.programme Master’s Programme in Computer, Communication and Information Sciences fi
local.aalto.electroniconly yes
local.aalto.openaccess yes


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search archive


Advanced Search

article-iconSubmit a publication

Browse

My Account