Classifying Partially Labeled Networked Data VIA Logistic Network Lasso
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Authors
Date
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
5
Series
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings, pp. 3832-3836, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings ; Volume 2020-May
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.Description
Keywords
Other note
Citation
Tran, N, Ambos, H & Jung, A 2020, Classifying Partially Labeled Networked Data VIA Logistic Network Lasso. in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings., 9054408, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2020-May, IEEE, pp. 3832-3836, IEEE International Conference on Acoustics, Speech, and Signal Processing, Barcelona, Spain, 04/05/2020. https://doi.org/10.1109/ICASSP40776.2020.9054408