A convex formulation for semi-supervised multi-label feature selection

Xiaojun Chang, Feiping Nie, Yi Yang, Heng Huang

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

173 Citations (Scopus)

Abstract

Explosive growth of multimedia data has brought challenge of how to efficiently browse, retrieve and organize these data. Under this circumstance, different approaches have been proposed to facilitate multimedia analysis. Several semi-supervised feature selection algorithms have been proposed to exploit both labeled and unlabeled data. However, they are implemented based on graphs, such that they cannot handle large-scale datasets. How to conduct semi-supervised feature selection on large-scale datasets has become a challenging research problem. Moreover, existing multi-label feature selection algorithms rely on eigen-decomposition with heavy computational burden, which further prevent current feature selection algorithms from being applied for big data. In this paper, we propose a novel convex semi-supervised multi-label feature selection algorithm, which can be applied to large-scale datasets. We evaluate performance of the proposed algorithm over five benchmark datasets and compare the results with state- of-the-art supervised and semi-supervised feature selection algorithms as well as baseline using all features. The experimental results demonstrate that our proposed algorithm consistently achieve superiors performances.

Original languageEnglish
Title of host publicationProceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence
Subtitle of host publicationJuly 27–31, 2014, Québec City, Québec, Canada
EditorsCarla E. Brodley, Peter Stone
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages1171-1177
Number of pages7
Volume2
ISBN (Electronic)9781577356783
Publication statusPublished - 2014
Externally publishedYes
EventAAAI Conference on Artificial Intelligence 2014 - Quebec, Canada
Duration: 27 Jul 201431 Jul 2014
Conference number: 28th
http://www.aaai.org/Conferences/AAAI/aaai14.php

Conference

ConferenceAAAI Conference on Artificial Intelligence 2014
Abbreviated titleAAAI 2014
Country/TerritoryCanada
CityQuebec
Period27/07/1431/07/14
Internet address

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