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A connectedness constraint for learning sparse graphs
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-6992-5771
KTH, School of Electrical Engineering (EES), Information Science and Engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Information Science and Engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-6855-5868
KTH, School of Electrical Engineering (EES), Information Science and Engineering. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2017 (English)In: 25th European Signal Processing Conference, EUSIPCO 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 151-155, article id 8081187Conference 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
Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 151-155, article id 8081187
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-224300DOI: 10.23919/EUSIPCO.2017.8081187Scopus ID: 2-s2.0-85041483337ISBN: 9780992862671 OAI: oai:DiVA.org:kth-224300DiVA, id: diva2:1190767
Conference
25th European Signal Processing Conference, EUSIPCO 2017, Kos International Convention CenterKos, Greece, 28 August 2017 through 2 September 2017
Note

QC 20180315

Available from: 2018-03-15 Created: 2018-03-15 Last updated: 2018-03-15Bibliographically approved

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Sundin, MartinJansson, Magnus

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