Regularized optimization applied to clustering and joint estimation of multiple undirected graphical models

Georgogiannis Alexandros

URI	http://purl.tuc.gr/dl/dias/E7D9F04C-CAE3-4DEF-858F-1A6B3BEF26EC	-
Identifier	https://doi.org/10.26233/heallink.tuc.21011	-
Language	en	-
Extent	62 pages	en
Title	Regularized optimization applied to clustering and joint estimation of multiple undirected graphical models	en
Creator	Georgogiannis Alexandros	en
Creator	Γεωργογιαννης Αλεξανδρος	el
Contributor [Thesis Supervisor]	Digalakis Vasilis	en
Contributor [Thesis Supervisor]	Διγαλακης Βασιλης	el
Contributor [Committee Member]	Liavas Athanasios	en
Contributor [Committee Member]	Λιαβας Αθανασιος	el
Contributor [Committee Member]	Lagoudakis Michael	en
Contributor [Committee Member]	Λαγουδακης Μιχαηλ	el
Publisher	Πολυτεχνείο Κρήτης	el
Publisher	Technical University of Crete	en
Academic Unit	Technical University of Crete::School of Electronic and Computer Engineering	en
Academic Unit	Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρονικών Μηχανικών και Μηχανικών Υπολογιστών	el
Description	Submitted to the School of Electronic and Computer Engineering in partial fulfillment of the requirements for the Master of Science degree	en
Content Summary	Since its earliest days as a discipline, machine learning has made use of optimization formulations and algorithms. Likewise, machine learning has contributed to optimization, driving the develop- ment of new optimization approaches that address the significant challenges presented by machine learning applications. This influence continues to deepen, producing a growing literature at the intersection of the two fields while attracting leading researchers to the effort. While techniques proposed twenty years ago continue to be refined, the increased complexity, size, and variety of today’s machine learning models demand a principled reassessment of existing assumptions and techniques. This thesis makes a small step toward such a reassessment. It describes novel contexts of established frameworks such as convex relaxation, splitting methods, and regularized estimation and how we can use them to solve significant problems in data mining and statistical learning. The thesis is organised in two parts. In the first part, we present a new clustering algorithm. The task of clustering aims at discovering structures in data. This algorithm is an extension of recently proposed convex relaxations of k-means and hierarchical clustering. In the second part, we present a new algorithm for discovering dependencies among common variables in multiple undirected graphical models. Graphical models are useful for the description and modelling of multivariate systems. In the appendix, we comment on a core problem underlying the whole study and we give an alternative solution based on recent advances in convex optimization.	en
Type of Item	Μεταπτυχιακή Διατριβή	el
Type of Item	Master Thesis	en
License	http://creativecommons.org/licenses/by/4.0/	en
Date of Item	2014-09-16	-
Date of Publication	2014	-
Subject	Optimization (Mathematics)	en
Subject	Optimization techniques	en
Subject	Optimization theory	en
Subject	Systems optimization	en
Subject	mathematical optimization	en
Subject	optimization mathematics	en
Subject	optimization techniques	en
Subject	optimization theory	en
Subject	systems optimization	en
Subject	Learning, Machine	en
Subject	machine learning	en
Subject	learning machine	en
Bibliographic Citation	Alexandros Georgogiannis, "Regularized optimization applied to clustering and joint estimation of multiple undirected graphical models", Master Thesis, School of Electronic and Computer Engineering, Technical University of Crete, Chania, Greece, 2014	en

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Regularized optimization applied to clustering and joint estimation of multiple undirected graphical models

Georgogiannis Alexandros

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