An Online Incremental Semi-Supervised Learning Method
Furao Shen*, Hui Yu*, Youki Kamiya**, and Osamu Hasegawa**
*The State Key Laboratory for Novel Software Technology, and Jiangyin Information Technology Research Institute, Nanjing University, Nanjing 210093, P.R. China
**The Imaging Science and Engineering Lab., Tokyo Institute of Technology, R2-52, 4259 Nagatsuda, Midori-ku, Yokohama 226-8503, Japan
Using labeled data and large amounts of unlabeled data, our proposed online incremental semisupervised learning automatically learns the topology of input data distribution without prior knowledge of numbers of nodes or network structure. Using labeled data, it labels generated nodes and divides a learned topology into substructures corresponding to classes. Node weights used as prototype vectors enable classification. New labeled or unlabeled data is added incrementally to the system during learning. Experimental results for artificial and real-world data show that this learning efficiently learns online incremental tasks even in noisy and non-stationary environments.
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