Semi-supervised Growing Neural Gas for Face Recognition

摘要

In many face recognition and other classification applications, there exist unlabelled data available for training along with labelled data. The use of unlabelled data can improve the performance of the classifier. In this paper, a semi-supervised growing neural gas is proposed for such applications. The classifier is first trained on the labelled data and then gradually unlabelled data is classified and added to the training data. The proposed algorithm is demonstrated, on both artificial and real datasets, to significantly boost the classification rate with the use of unlabelled data. The improvement is particularly great when the labelled dataset is small. The algorithm is computationally simple and easy to implement.