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Unsupervised Feature Selection for Linked Social Media Data

Unsupervised Feature Selection for Linked Social Media Data. Jiliang Tang, Huan Liu Arizona State University. Motivations. Feature selection is effective in dealing with high-dimensional data.

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Unsupervised Feature Selection for Linked Social Media Data

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  1. Unsupervised Feature Selection for Linked Social Media Data Jiliang Tang, Huan Liu Arizona State University

  2. Motivations • Feature selection is effective in dealing with high-dimensional data. • Absence of label information associated with the features. (Traditional unsupervised feature selection algorithms fail.) • Link information in the social media data could be useful.

  3. Linked Data in Social Media

  4. Linked Unsupervised Feature Selection(LUFS)

  5. Experiments

  6. Source Free (proposal) - Open to discussion

  7. General Framework

  8. 2-Step iterative learning • Learn for the labeled training data & unlabeled data. • Retrieve the unlabeled data from open database, such as Web etc.

  9. Preliminary studyon text classification tasks

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