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.


Linked data in s ocial m edia
Linked Data in Social Media




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.


Preliminary study on text classification tasks
Preliminary studyon text classification tasks


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