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| Linked Social Media Data Based Semi-Supervised Feature Selection Method |
| WANG Yi-Bing, PAN Zhi-Song, WU Jun-Qing, JIA Bo, HU Gu-Yu |
| College of Command Information System, PLA University of Science and Technology, Nanjing 210007 |
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Abstract Mountains of high-dimensional, unlabeled data are produced by the social media network, which brings tremendous challenges to the data processing. Meanwhile, the linked graph information between data samples can not be effectively used in the existing pattern recognition algorithms. A semi-supervised feature selection method (SSLFS) based on linked relations is proposed combined with a little supervised information after mining the linked graph of social media network. Through spectral analysis and sparsity constraint, SSLFS selects feature subsets which maintain the characteristics of local manifold and sparsity. The experimental results on the Flickr dataset show that the subset obtained by SSLFS is more effective when applied to classification compared with those by other methods.
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Received: 13 May 2013
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2014, Vol. 27 
