Abstract
In many classification applications and face recognition tasks, there exist unlabelled data available for training along with labelled samples. The use of unlabelled data can improve the performance of a classifier. In this paper, a semi-supervised growing neural gas is proposed for learning with such partly labelled datasets in face recognition applications. The classifier is first trained on the labelled data and then gradually unlabelled data is classified and added to the training data. The classifier is retrained; and so on. The proposed iterative algorithm conforms to the EM framework and 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. Comparison with support vector machine classifiers is also given. The algorithm is computationally efficient and easy to implement.
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Zaki, S.M., Yin, H. A Semi-Supervised Learning Algorithm for Growing Neural Gas in Face Recognition. J Math Model Algor 7, 425–435 (2008). https://doi.org/10.1007/s10852-008-9095-8
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DOI: https://doi.org/10.1007/s10852-008-9095-8