Compressed sensing for big data over complex networks

Thumbnail Image

Files

master_Basirian_Jahromi_Saeed_2018.pdf (641.79 KB)

URL

Journal Title

Journal ISSN

Volume Title

Perustieteiden korkeakoulu | Master's thesis
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

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

Other note

Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-201801261175

Collections

[dipl] Perustieteiden korkeakoulu / SCI