A Network Compatibility Condition for Compressed Sensing over Complex Networks

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Abstract

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
PublisherIEEE
Pages50-54
Number of pages5
ISBN (Print)9781538615706
DOIs
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

Workshop

WorkshopIEEE Statistical Signal Processing Workshop
Abbreviated titleSSP
CountryGermany
CityFreiburg im Breisgau
Period10/06/201813/06/2018

Keywords

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

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  • Cite this

    Tran, N., Ambos, H., & Jung, A. (2018). A Network Compatibility Condition for Compressed Sensing over Complex Networks. In 2018 IEEE Statistical Signal Processing Workshop, SSP 2018 (pp. 50-54). [8450811] IEEE. https://doi.org/10.1109/SSP.2018.8450811