Abstract
We present an Incremental Local Distribution Network (ILDN) for unsupervised learning, which combines the merits of matrix learning and incremental learning. It stores local distribution information in each node with covariant matrix and uses a vigilance parameter with statistical support to decide whether to extend the network. It has a statistics based merging mechanism and thus can obtain a precise and concise representation of the learning data called relaxation representation. Moreover, the denoising process based on data density makes ILDN robust to noise and practically useful. Experiments on artificial and real-world data in both “closed” and “open-ended” environment show the better accuracy, conciseness, and efficiency of ILDN over other methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lloyd, S.P.: Least squares quantization in PCM. IEEE Transactions on Information Theory 28(2), 129–137 (1982)
Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: Neural gas network for vector quantization and its application to timeseries prediction. IEEE Transactions on Neural Networks 4(4), 556–558 (1993)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 43(1), 59–69 (1982)
Martinetz, T., Schulten, K.: Topology representing networks. Neural Networks 7(3), 507–552 (1994)
López-Rubio, E., Muñoz-Pérez, J., Gómez-Ruiz, J.A.: A pricipal components analysis self-organizing map. Neural Networks 17, 261–270 (2004)
Yin, H., Allinson, N.M.: Self-organizing mixture networks for probability density estimation. IEEE Transactions on Neural Networks 12(2), 405–411 (2001)
Huang, D., Yi, Z., Pu, X.: A new local PCA-SOM algorithm. Neurocomputing 71, 3544–3552 (2008)
Arnonkijpanich, B., Hasenfuss, A., Hammer, B.: Local matrix adaptation in topographic neural maps. Neurocomputing 74, 522–539 (2011)
López-Rubio, E.: Probabilistic self-organizing maps for continuous data. IEEE Transactions on Neural Networks 21(10), 1543–1554 (2010)
Carpenter, G.A., Grossberg, S.: The ART of adaptive pattern recognition by self-organising neural network. IEEE Computer 21(3), 77–88 (1988)
Fritzke, B.: A growing neural gas network learns topologies. In: Proceedings of the 1995 Advances in Neural Information Processing Systems, pp. 625–632 (1995)
Alahakoon, D., Halgamuge, S.K., Srinivasan, B.: Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Transactions on Neural Networks 11(3), 601–614 (2000)
Marsland, S., Shapiro, J., Nehmzow, U.: A self-organising network that grows when required. Neural Networks 15(8–9), 1041–1058 (2002)
Shen, F., Hasegawa, O.: A fast nearest neighbor classifier based on self-organizing incremental neural network. Neural Networks 21, 1537–1547 (2008)
Tscherepanow, M., Kortkamp, M., Kammer, M.: A hierarchical ART network for the stable incremental learning of topological structures and associations from noisy data. Neural Networks 24(8), 906–916 (2011)
Araujo, A.F.R., Rego, R.L.M.E.: Self-organizing maps with a time-varying structure. ACM Computing Surveys 46(1) Article No. 7 (2013)
Kristan, M., Leonardis, A., Skočaj, D.: Multivariate online kernel density estimation with Gaussian kernels. Pattern Recognition 44(10–11), 2630–2642 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xing, Y., Cao, T., Zhou, K., Shen, F., Zhao, J. (2015). An Incremental Local Distribution Network for Unsupervised Learning. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9077. Springer, Cham. https://doi.org/10.1007/978-3-319-18038-0_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-18038-0_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18037-3
Online ISBN: 978-3-319-18038-0
eBook Packages: Computer ScienceComputer Science (R0)