Compressed Sensing for Big Data Over Complex Networks

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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu | Master's thesis
Date
2018-01-22
Department
Major/Subject
Machine Learning and Data Mining
Mcode
SCI3044
Degree programme
Master’s Programme in Computer, Communication and Information Sciences
Language
en
Pages
39 + 7
Series
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.
Description
Supervisor
Jung, Alexander
Thesis advisor
Jung, Alexander
Keywords
semi-supervised learning, graph signal learning, probabilistic graphical models, approximate inference, loopy belief propagation, complex networks
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