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Unsupervised Feature Selection for Linked Social Media Data

Unsupervised Feature Selection for Linked Social Media Data. Jiliang Tang and Huan Liu Computer Science and Engineering Arizona State University August 12-16, 2012 KDD2012. Social Media. The expansive use of social media generates massive data in an unprecedented rate

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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 and Huan Liu Computer Science and Engineering Arizona State University August 12-16, 2012 KDD2012

  2. Social Media • The expansive use of social media generates massive data in an unprecedented rate - 250 million tweets per day - 3,000 photos in Flickr per minute -153 million blogs posted per year

  3. High-dimensional Social Media Data • Social Media Data can be high-dimensional • Photos • Video stream • Tweets • Presenting new challenges • Massive and noisy data • Curse of dimensionality

  4. Feature Selection • Feature selection is an effective way of preparing high-dimensional data for efficient data mining. • What is new for feature selection of social media data?

  5. Representation of Linked Data …. …. ….

  6. Challenges for Feature Selection • Unlabeled data - No explicit definition of feature relevancy - Without additional constraints, many subsets of features could be equally good • Linked data - Not independent and identically distributed

  7. Opportunities for Feature Selection • Social media data provides link information - Correlation between data instances • Social media data provides extra constraints - Enabling us to exploring the use of social theories

  8. Problem Statement • Given n linked data instances, its attribute-value representation X, its link representation R, we want to select a subset of features by exploiting both X and R for these n data instances in an unsupervised scenario.

  9. Supervised and Unsupervised Feature Selection • A unified view • Selecting features that areconsistent with some constraints for either supervised or unsupervised feature selection • Class labels are sort of targets as a constraint • Two problems for unsupervised feature selection - What are the targets? - Where can we find constraints?

  10. Our Framework: LUFS

  11. The Target for LUFS

  12. The Constraints for LUFS

  13. Pseudo-class Label • s is a selection vector - s(j) = 1 if j-thfeature is selected, s(j)=0 otherwise - , X = diag(s)X • Y is the pseudo-class label indicator matrix - Y = - ||Y(:,i)=

  14. Social Dimension for Link Information • Social Dimension captures group behaviors of linked Instances • Instances in different social dimensions are disimilar • Instances within a social dimension are similar • Example:

  15. Social Dimension Regularization • Within, between, and total social dimension scatter matrices, • Instances are similar within social dimensions while dissimilar between social dimensions.

  16. Constraint from Attribute-Value Data • Similar instances in terms of their contents are more likely to share similar topics,

  17. An Optimization Problem for LUFS

  18. The Optimization Problem for LUFS

  19. The Optimization Problem for LUFS

  20. The Optimization Problem for LUFS

  21. LUFS after Two Relaxations • Spectral Relaxation on Y - Social Dimension Regularization: • W= diag(s)W, and adding 2,1-norm on W

  22. Evaluating LUFS • Datasets and experiment setting • What is the performance of LUFS comparing to state-of-the art baseline methods? • Why does LUFS work?

  23. Evaluating LUFS • Datasets and experiment setting • What is the performance of LUFS comparing to state-of-the art baseline methods? • Why does LUFS work?

  24. Data and Characteristics • Flickr • BlogCatalog

  25. http://dmml.asu.edu/users/xufei/datasets.html

  26. Experiment Settings • Metrics - Clustering: Accuracy and NMI - K-Means • Baseline methods - UDFS - SPEC - Laplacian Score

  27. Evaluating LUFS • Datasets and experiment setting • What is the performance of LUFS comparing to state-of-the art baseline methods? • Why does LUFS work?

  28. Results on Flickr

  29. Results on Flickr

  30. Results on BlogCatalog

  31. Evaluating LUFS • Datasets and experiment setting • What is the performance of LUFS comparing to state-of-the art baseline methods? • Why does LUFS work?

  32. Probing Further: Why Social Dimensions Work Social Dimension Extraction Link Information Random Assignment ……. ……. Random Groups Social Dimensions

  33. Results in Flickr

  34. Future Work • Further exploration of link information • Noise and incomplete social media data • Other sources: multi-view sources • The strength of social ties ( strong and weak ties mixed)

  35. More Information? http://www.public.asu.edu/~huanliu/projects/NSF12/

  36. Questions Acknowledgments: This work is, in part, sponsored by National Science Foundation via a grant (#0812551). Comments and suggestions from DMML members and reviewers are greatly appreciated.

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