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Label Propagation for Learning with Label Proportions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP 2018)
Subtitle of host publicationProceedings of a meeting held 17-20 September 2018, Aalborg, Denmark
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Pages264-270
Number of pages6
ISBN (Electronic)9781538654774
ISBN (Print)9781538654781
DOIs
DateAccepted/In press - 17 Sep 2018
DateE-pub ahead of print - 1 Nov 2018
DatePublished (current) - Nov 2018

Publication series

Name
ISSN (Print)1551-2541

Abstract

Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags. This paradigm is particularly suitable in contexts where providing individual labels is expensive and label aggregates are more easily obtained. In the healthcare domain, it is a burden for a patient to keep a detailed diary of their daily routines, but often they will be amenable to provide higher level summaries of daily behavior. We present a novel and efficient graph-based algorithm that encourages local smoothness and exploits the global structure of the data, while preserving the 'mass' of each bag.

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    Rights statement: This is the final published version of the article (version of record). It first appeared online via IEEE at https://ieeexplore.ieee.org/document/8517083 . Please refer to any applicable terms of use of the publisher.

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    Licence: CC BY-NC-SA

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