JACIII Vol.14 No.6 pp. 593-605
doi: 10.20965/jaciii.2010.p0593


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

December 21, 2009
May 28, 2010
September 20, 2010
online incremental learning, labeled data and unlabeled data, semi-supervised learning, non-stationary environment

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.

Cite this article as:
Furao Shen, Hui Yu, Youki Kamiya, and Osamu Hasegawa, “An Online Incremental Semi-Supervised Learning Method,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.6, pp. 593-605, 2010.
Data files:
  1. [1] S. Grossberg, “Nonlinear neural networks: principles, mechanisms, and architectures,” Neural Networks, Vol.1, pp. 17-61, 1988.
  2. [2] F. H. Hamker, “Life-long Learning Cell Structures – Continuously Learning without Catastrophic Interference,” Neural Networks, Vol.14, No.4, pp.551-572, 2001.
  3. [3] F. Shen and O. Hasegawa, “An Incremental Network for On-line Unsupervised Classification and Topology Learning,” Neural Networks, Vol.19, No.1, pp. 90-106, 2006.
  4. [4] O. Chapelle, B. Schölkopf, and A. Zien, editors, “Semi-Supervised Learning,” MIT Press, 2006.
  5. [5] X. Zhu, “Semi-Supervised Learning Literature Survey,” Technical Report 1530, University of Wisconsin, Madison, 2005.
  6. [6] A. Dong and B. Bhanu, “Active Concept Learning in Image Databases,” IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICSBłPART B: CYBERNETICS, Vol.35, No.3, pp. 450-466, 2005.
  7. [7] J. Tang, X.-S. Hua, M. Wang, Z. Gu, G.-J. Qi, and X. Wu, “Correlative Linear Neighborhood Propagation for Video Annotation,” IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICSBłPART B: CYBERNETICS, Vol.39, No.2, pp. 409-416, 2009.
  8. [8] T. Kohonen, editor, “Self-Organizing Maps,” Springer Series in Information Sciences, Springe, Heidelberg, 1997.
  9. [9] H. Bauer and T. Villmann, “Growing a hypercubical output space in a self-organizing feature map,” IEEE Trans. on Neural Networks, Vol.8, No.2, pp. 218-226, March 1997.
  10. [10] B. Fritzke, “Growing Cell Structures – A Self-organizing Network for Unsupervised and Supervised Learning,” Neural Networks, Vol.7, No.9, pp. 1441-1460, 1994.
  11. [11] B. Fritzke, “A Growing Neural Gas Network Learns Topologies,” In Neural Information Processing Systems, Vol.7, pp. 625-632, Denver, USA, 1995, MIT Press.
  12. [12] Y. Prudent and A. Ennaji, “An Incremental Growing Neural Gas Learns Topologies,” In Proc. of the IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks (IJCNN ’05), pp. 1211-1216, Montreal, Canada, 2005.
  13. [13] S. Grossberg, “Adaptive Pattern Recognition and Universal Encoding II: Feedback, Expectation, Olfaction, and Illusions,” Biological Cybernetics, Vol.23, pp. 187-202, 1976.
  14. [14] G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, “Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps,” IEEE Trans. on Neural Networks, Vol.3, No.5, pp. 698-713, 1992.
  15. [15] G. C. Anagnostopoulos, M. Bharadwaj, M. Georgiopoulos, S. J. Verzi, and G. L. Heileman, “Exemplar-based Pattern Recognition via Semi-Supervised Learning,” In Proc. of the IEEE-INNSENNS Int. Joint Conf. on Neural Networks (IJCNN ’03), Vol.3, pp. 1350-1356, 2003.
  16. [16] F. Shen, T. Ogura, and O. Hasegawa, “An enhanced self-organizing incremental neural network for online unsupervised learning,” Neural Networks, Vol.20, pp. 893-903, 2007.
  17. [17] T. M. Martinetz, “Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps,” In Proc. of Int. Conf. on Artificial Neural Network (ICANN), pp. 427-434, 1993.
  18. [18] T. M. Martinetz and K. J. Schulten, “Topology Representing Networks,” Neural Networks, Vol.7, No.3, pp. 507-522, 1994.
  19. [19] V. Sindhwani, P. Niyogi, and M. Belkin, “Beyond the Point Cloud: from Transductive to Semi-Supervised Learning,” In Proc. of the 22nd Int. Conf. onMachine Learning (ICML 05), pp. 824-831, New York, NY, USA, 2005, ACM Press.
  20. [20] T. M. Cover and P. E. Hart, “Nearest Neighbor Pattern Classification,” IEEE Trans. on Information Theory, IT-13, No.1, pp. 21-27, 1967.
  21. [21] C. Merz and M. Murphy, “UCI Repository of Machine Learning Databases,” Technical report, University of California Department of Information, Irvine, CA.
  22. [22] A. P. Bradley, “The Use of the Area Under the ROC curve in the Evaluation of Machine Learning Algorithms”, Pattern Recognition, Vol.30, No.7, pp. 1145-1159, 1997.
  23. [23] V. Vapnik, editor, “Statistical Learning Theory,” Wiley, New York, 1998.

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