Unsupervised feature selection for multi-view data in social media

Jiliang Tang, Xia Hu, Huiji Gao, Huan Liu

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


The explosive popularity of social media produces mountains of high-dimensional data and the nature of social media also determines that its data is often unla-belled, noisy and partial, presenting new challenges to feature selection. Social media data can be represented by heterogeneous feature spaces in the form of multiple views. In general, multiple views can be complementary and, when used together, can help handle noisy and partial data for any single-view feature selection. These unique challenges and properties motivate us to develop a novel feature selection framework to handle multi-view social media data. In this paper, we investigate how to exploit relations among views to help each other select relevant features, and propose a novel unsupervised feature selection framework, MVFS, for multi-view social media data. We systematically evaluate the proposed framework in multi-view datasets from social media websites and the results demonstrate the effectiveness and potential of MVFS.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2013, SMD 2013
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Print)9781627487245
StatePublished - 2013
Event13th SIAM International Conference on Data Mining, SMD 2013 - Austin, United States
Duration: May 2 2013May 4 2013


Other13th SIAM International Conference on Data Mining, SMD 2013
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Signal Processing
  • Software


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