Abstract
The increasingly popular social media produces large amount of high-dimensional data every day. Feature selection is of great importance in data mining tasks. Social media data differ from traditional attribute-value data since it is linked. A graph-based semi-supervised feature selection for social media data is proposed in this paper, which uses two graphs: nearest neighborhood graph and link relationships graph to describe the underlying local and global structure of samples, respectively. And the features which have good ability on local manifold structure and global link relationships are preserved. The experimental results on real-world social media data show its efficiency and validity of the proposed method.
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References
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mech Learn Res 3:1157–1182
Tang J, Liu H (2012) Unsupervised feature selection for linked social media data. KDD, pp 904–912
Tang J, Liu H (2012) Feature selection with linked data in social media. SDM, pp 118–128
McPherson M, Lovin LS, Cook JM (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27(1):415–444
Marsden P, Friedkin N (1993) Network studies of social influence. Sociol Methods Res 22(1):127–151
Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434
Zhao Z, Liu H (2007) Spectral feature selection for supervised and unsupervised learning. In: Proceedings of the 24th international conference on machine learning, pp 1151–1157
Naruchitparames J, Gunes M, Louis S (2011) Friend recommendations in social networks using genetic algorithms and network topology. In: IEEE congress on evolutionary computation (CEC), pp 2207–2214
Liu J, Ji S, Ye J (2009) Multi-task feature learning via efficient L2,1-norm minimization. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, pp 339–348
Wang X, Tang L, Gao H, Liu H (2010) Discovering overlapping groups in social media. In: 2010 IEEE international conference on data mining, pp 569–578
Acknowledgments
This work is supported by the National Science Foundation of China under Grant No. 60902069 and No. 61171124, supported by Science Technology Planning Project of Guangdong (Grant No. 2011B010200045) and supported by Science Technology Planning Project of Shenzhen (Grant No. JCYJ20130329110601621).
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Wang, N., Liu, Z., Li, X. (2014). Graph-Based Semi-supervised Feature Selection for Social Media Data. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_11
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DOI: https://doi.org/10.1007/978-3-642-54924-3_11
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