When is Network Lasso Accurate: The Vector Case

Nguyen Tran Quang, Saeed Basirian Jahromi, Alex Jung

Research output: Contribution to conferencePaperProfessional

Abstract

A recently proposed learning algorithm for massive network-structured data sets
(big data over networks) is the network Lasso (nLasso), which extends the wellknown Lasso estimator from sparse models to network-structured datasets. Efficient implementations of the nLasso have been presented using modern convex optimization methods. In this paper we provide sufficient conditions on the network structure and available label information such that nLasso accurately learns a vector-valued graph signal (representing label information) from the information provided by the labels of a few data points.
Original languageEnglish
Number of pages5
Publication statusPublished - 2017
MoE publication typeNot Eligible
EventIEEE Conference on Neural Information Processing Systems - Long Beach, United States
Duration: 4 Dec 20179 Dec 2017
Conference number: 31

Conference

ConferenceIEEE Conference on Neural Information Processing Systems
Abbreviated titleNIPS
CountryUnited States
CityLong Beach
Period04/12/201709/12/2017

Fingerprint Dive into the research topics of 'When is Network Lasso Accurate: The Vector Case'. Together they form a unique fingerprint.

  • Cite this

    Tran Quang, N., Basirian Jahromi, S., & Jung, A. (2017). When is Network Lasso Accurate: The Vector Case. Paper presented at IEEE Conference on Neural Information Processing Systems, Long Beach, United States.