unsupervised feature selection for linked social media data
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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

Unsupervised Feature Selection for Linked Social Media Data

Jiliang Tang, Huan Liu

Arizona State University

motivations
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.
source free proposal

Source Free (proposal)

- Open to discussion

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