Towards feature selection in network

Quanquan Gu, Jiawei Han

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


Traditional feature selection methods assume that the data are independent and identically distributed (i.i.d.). However, in real world, there are tremendous amount of data which are distributing in a network. Existing features selection methods are not suited for networked data because the i.i.d. assumption no longer holds. This motivates us to study feature selection in a network. In this paper, we present a supervised feature selection method based on Laplacian Regularized Least Squares (LapRLS) for networked data. In detail, we use linear regression to utilize the content information, and adopt graph regularization to consider the link information. The proposed feature selection method aims at selecting a subset of features such that the empirical error of LapRLS is minimized. The resultant optimization problem is a mixed integer programming, which is difficult to solve. It is relaxed into a L 2,1-norm constrained LapRLS problem and solved by accelerated proximal gradient descent algorithm. Experiments on benchmark networked data sets show that the proposed feature selection method outperforms traditional feature selection method and the state of the art learning in network approaches.

Original languageEnglish (US)
Title of host publicationCIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management
Number of pages10
StatePublished - Dec 13 2011
Event20th ACM Conference on Information and Knowledge Management, CIKM'11 - Glasgow, United Kingdom
Duration: Oct 24 2011Oct 28 2011

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Other20th ACM Conference on Information and Knowledge Management, CIKM'11
Country/TerritoryUnited Kingdom


  • Laplacian regularized least squares
  • feature selection
  • graph regularization
  • network

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)


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