Positive and Unlabeled Generative Adversarial Network on POI Positioning

With the rapid popularization of smart mobile devices, people rely more and more on location-based social networking service (LBSNS). Due to the high cost of data acquisition, point of interest (POI) positioning based on small data collection has become a big challenge. Recent research focuses on received signal strength (RSS) and simultaneous localization methods. Although there has been some research on POI positioning, the existing approaches do not discuss the problem of insufficient positive training samples. Based on the truthful positive data and a large amount of unlabeled data, a novel approach, called positive and unlabeled generative adversarial network (puGAN), is proposed. Firstly, we use positive and unlabeled method along with the generative adversarial network to effectively mine the hidden features of data. Secondly, based on the hidden features, we calibrate the positive data and unlabeled data, then treat them as the input of the discriminator. Finally, with the minimax of generator and discriminator, a POI-discriminator model is obtained. We evaluate the new method by analyzing ROC curve and the relationship between training error and testing error. The results of experiments show that the method we proposed can effectively solve the problem of insufficient positive samples and outperforms the traditional models of POI positioning, including one-class classifier, SVM and neural network.