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A Connectedness Constraint for Learning Sparse Graphs
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-6992-5771
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-6855-5868
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-2638-6047
2017 (English)In: 2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), IEEE , 2017, p. 151-155Conference paper, Published paper (Refereed)
Abstract [en]

Graphs are naturally sparse objects that are used to study many problems involving networks, for example, distributed learning and graph signal processing. In some cases, the graph is not given, but must be learned from the problem and available data. Often it is desirable to learn sparse graphs. However, making a graph highly sparse can split the graph into several disconnected components, leading to several separate networks. The main difficulty is that connectedness is often treated as a combinatorial property, making it hard to enforce in e.g. convex optimization problems. In this article, we show how connectedness of undirected graphs can be formulated as an analytical property and can be enforced as a convex constraint. We especially show how the constraint relates to the distributed consensus problem and graph Laplacian learning. Using simulated and real data, we perform experiments to learn sparse and connected graphs from data.

Place, publisher, year, edition, pages
IEEE , 2017. p. 151-155
Series
European Signal Processing Conference, ISSN 2076-1465
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-226274ISI: 000426986000031Scopus ID: 2-s2.0-85041483337ISBN: 978-0-9928-6267-1 OAI: oai:DiVA.org:kth-226274DiVA, id: diva2:1198964
Conference
25th European Signal Processing Conference (EUSIPCO), AUG 28-SEP 02, 2017, GREECE
Note

QC 20180419

Available from: 2018-04-19 Created: 2018-04-19 Last updated: 2018-04-19Bibliographically approved

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Sundin, MartinJansson, MagnusChatterjee, Saikat

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