The nature of statistical learning theory
The nature of statistical learning theory
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Convex Optimization
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
An accelerated gradient method for trace norm minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Relation regularized matrix factorization
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multi-task feature learning via efficient l2, 1-norm minimization
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Combining link and content for collective active learning
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Exploiting social relations for sentiment analysis in microblogging
Proceedings of the sixth ACM international conference on Web search and data mining
Social spammer detection in microblogging
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.