Towards feature selection in network

Traditional feature selection methods assume that the data are independent and identically distributed (i.i.d.). In real world, tremendous amounts of data are distributed 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. 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 L2,1-norm constrained LapRLS problem and solved by accelerated proximal gradient descent algorithm. Experiments on benchmark n...

• Type: Journal
• Year: 2011
• Where: CIKM
• Authors: Quanquan Gu, Jiawei Han
• Keywords: CIKM 2011, Empirical Error, Gradient Descent, Information Technology, Network Graph