A Network Compatibility Condition for Compressed Sensing over Complex Networks

Nguyen Tran, Henrik Ambos, Alexander Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)
128 Downloads (Pure)


This paper continues our recently initiated line of work on analyzing the network Lasso (nLasso, which has been proposed as an efficient learning algorithm for massive networkstructured data sets (big data over networks). The nLasso extends the well-known Lasso estimator to network-structured datasets. In this paper we consider the nLasso using squared error loss and provide sufficient conditions on the network structure and available label information such that nLasso accurately recovers a clustered (piece-wise constant) graph signal (representing label information) from the information pro-vided by the labels of a few data points.

Original languageEnglish
Title of host publication2018 IEEE Statistical Signal Processing Workshop, SSP 2018
Number of pages5
ISBN (Print)9781538615706
Publication statusPublished - 29 Aug 2018
MoE publication typeA4 Article in a conference publication
EventIEEE Statistical Signal Processing Workshop - Freiburg im Breisgau, Germany
Duration: 10 Jun 201813 Jun 2018
Conference number: 20


WorkshopIEEE Statistical Signal Processing Workshop
Abbreviated titleSSP
CityFreiburg im Breisgau


  • big data over networks
  • complex networks
  • compressed sensing
  • network compatibility condition
  • network Lasso

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