Classifying Partially Labeled Networked Data VIA Logistic Network Lasso

Nguyen Tran, Henrik Ambos, Alexander Jung

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

3 Citations (Scopus)

Abstract

We apply the network Lasso to classify partially labeled data points which are characterized by high-dimensional feature vectors. In order to learn an accurate classifier from limited amounts of labeled data, we borrow statistical strength, via an intrinsic network structure, across the dataset. The resulting logistic network Lasso amounts to a regularized empirical risk minimization problem using the total variation of a classifier as a regularizer. This minimization problem is a nonsmooth convex optimization problem which we solve using a primal-dual splitting method. This method is appealing for big data applications as it can be implemented as a highly scalable message passing algorithm.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherIEEE
Pages3832-3836
Number of pages5
ISBN (Electronic)9781509066315
DOIs
Publication statusPublished - May 2020
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Virtual conference, Barcelona, Spain
Duration: 4 May 20208 May 2020
Conference number: 45

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
CountrySpain
CityBarcelona
Period04/05/202008/05/2020
OtherVirtual conference

Fingerprint Dive into the research topics of 'Classifying Partially Labeled Networked Data VIA Logistic Network Lasso'. Together they form a unique fingerprint.

Cite this