Semi-supervised Learning with Graphs can achieve good results in classification tasks even in difficult conditions. Unfortunately, it can be slow and use a lot of memory. The first important step of the graph-based semi-supervised learning approaches is the construction of the graph from the data, where each data-point usually becomes a vertex in the graph - a potential problem with large amounts of data. In this paper, we present a graph construction method that uses an unsupervised neural network called growing neural gas (GNG). The GNG instance presents a intelligent stopping criteria that determines when the final network configuration maps correctly the input-data points. With that in mind, we use the final trained network as a reduced input graph for the semi-supervised classification algorithm, associating original data-points to the neurons they have activated in the unsupervised training process. © 2013 IEEE. 2430Zhu, X., (2005) Semi-supervised Learning with Graphs, , Ph.D. dissertation, Carnegie Mellon UniversityZhu, X., Goldberg, A.B., (2009) Introduction to Semi-Supervised Learning, , Morgan & Claypool PublishersZhu, X., Semi-supervised learning literature survey (2008) SurveyFergus, R., Weiss, Y., Torralba, A., Semi-supervised learning in gigantic image collections (2009) Advances in Neural Information Processing Systems (NIPS)Fritzke, B., A growing neural gas network learns topologies (1995) Advances in Neural Information Processing Systems 7. MIT Press, pp. 625-632Graham, J., Starzyk, J., A hybrid self-organizing neural gas based network (2008) Neural Networks 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on, pp. 3806-3813Sledge, I., Keller, J., Growing neural gas for temporal clustering (2008) Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, pp. 1-4Blum, A., Lafferty, J., Rwebangira, M.R., Reddy, R., Semisupervised learning using randomized mincuts (2004) Proceedings of the 21st International Conference on Machine Learning, pp. 97-104Talwalkar, A., Kumar, S., Rowley, H., Large-scale manifold learning (2008) Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pp. 1-8Mahdaviani, M., De Freitas, N., Fraser, B., Hamze, F., Fast computational methods for visually guided robots (2005) ICRA'05, pp. 138-143Zhu, X., Lafferty, J., Harmonic mixtures: Combining mixture models and graph-based methods for inductive and scalable semi-supervised learning (2005) Proc. Int. Conf. Machine Learning. ACM Press, pp. 1052-1059Chavez, D., Laures, G., Loayza, K., Patino, R., A stopping criteria for the growing neural gas based on a validity separation index for clusters (2011) Hybrid Intelligent Systems (HIS), 2011 11th International Conference on, pp. 578-583Zhu, X., Ghahramani, Z., Lafferty, J., Semi-supervised learning using gaussian fields and harmonic functions (2003) ICML, pp. 912-919Krizhevsky, A., Learning multiple layers of features from tiny images (2009) Master's Thesis, , http://www.cs.toronto.edu/kriz/learning-features-2009-TR.pdfTorralba, A., Fergus, R., Freeman, W.T., 80 million tiny images: A large data set for nonparametric object and scene recognition (2008) IEEE Trans. Pattern Anal. Mach. Intell., 30 (11), pp. 1958-1970. , http://dx.doi.org/10.1109/TPAMI.2008.128, NovOliva, A., Torralba, A., Modeling the shape of the scene: A holistic representation of the spatial envelope (2001) International Journal of Computer Vision, 42, pp. 145-175