An unsupervised approach for feature selection in linked data

Most of the data in the field of social media has many features. Accordingly, one of the main challenges in this field is processing such high-dimensional data. Researchers are motivated to propose novel approaches in order to overcome this problem. One of the best solutions is extracting the effective information from data pool and discard unnecessary one. Feature selection is a known technique which aims to distinguish discriminative features. Because of the unlabeled nature of datasets in social network, an Unsupervised Feature Selection algorithm might be a good scenario. In addition to features information, we confront inherently linked users in social network datasets. This is because a stronger unsupervised feature selection is needed which ignores the independent and identically distributed assumption. Hence, by optimizing a novel objective function in this paper, feature ranking is done and top features are extracted for further processing. This objective function incorporates both the relationship between users and information of users' features. The experimental results on real-world social network datasets demonstrate the effectiveness of our proposed approach.