Classifying Big Data over Networks Via the Logistic Network Lasso

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Abstract

We apply network Lasso to solve binary classification and clustering problems on network structured data. In particular we generalize ordinary logistic regression to non-Euclidean data defined over a complex network structure. The resulting logistic network Lasso classifier amounts to solving a convex optimization problem. A scalable classification algorithm is obtained by applying the alternating direction methods of multipliers.

Original languageEnglish
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
Pages855-858
Number of pages4
Volume2018-October
ISBN (Electronic)9781538692189
DOIs
Publication statusPublished - 19 Feb 2019
MoE publication typeA4 Article in a conference publication
EventAsilomar Conference on Signals, Systems & Computers - Pacific Grove, United States
Duration: 28 Oct 201831 Oct 2018
Conference number: 52

Conference

ConferenceAsilomar Conference on Signals, Systems & Computers
Abbreviated titleACSSC
CountryUnited States
CityPacific Grove
Period28/10/201831/10/2018

Keywords

  • big data over networks
  • classification
  • clustering
  • complex networks
  • compressed sensing
  • convex optimization
  • semi-supervised learning

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  • Cite this

    Ambos, H., Tran, N., & Jung, A. (2019). Classifying Big Data over Networks Via the Logistic Network Lasso. In M. B. Matthews (Ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 (Vol. 2018-October, pp. 855-858). [8645260] https://doi.org/10.1109/ACSSC.2018.8645260