Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

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

Georgogiannis Alexandros

Simple record


URIhttp://purl.tuc.gr/dl/dias/E7D9F04C-CAE3-4DEF-858F-1A6B3BEF26EC-
Identifierhttps://doi.org/10.26233/heallink.tuc.21011-
Languageen-
Extent62 pagesen
TitleRegularized optimization applied to clustering and joint estimation of multiple undirected graphical modelsen
CreatorGeorgogiannis Alexandrosen
CreatorΓεωργογιαννης Αλεξανδροςel
Contributor [Thesis Supervisor]Digalakis Vasilisen
Contributor [Thesis Supervisor]Διγαλακης Βασιληςel
Contributor [Committee Member]Liavas Athanasiosen
Contributor [Committee Member]Λιαβας Αθανασιοςel
Contributor [Committee Member]Lagoudakis Michaelen
Contributor [Committee Member]Λαγουδακης Μιχαηλel
PublisherΠολυτεχνείο Κρήτηςel
PublisherTechnical University of Creteen
Academic UnitTechnical University of Crete::School of Electronic and Computer Engineeringen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Ηλεκτρονικών Μηχανικών και Μηχανικών Υπολογιστώνel
DescriptionSubmitted to the School of Electronic and Computer Engineering in partial fulfillment of the requirements for the Master of Science degreeen
Content SummarySince 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 ItemΜεταπτυχιακή Διατριβήel
Type of ItemMaster Thesisen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2014-09-16-
Date of Publication2014-
SubjectOptimization (Mathematics)en
SubjectOptimization techniquesen
SubjectOptimization theoryen
SubjectSystems optimizationen
Subjectmathematical optimizationen
Subjectoptimization mathematicsen
Subjectoptimization techniquesen
Subjectoptimization theoryen
Subjectsystems optimizationen
SubjectLearning, Machineen
Subjectmachine learningen
Subjectlearning machineen
Bibliographic CitationAlexandros 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, 2014en

Available Files

Services

Statistics