Abstract
In this article we present an incremental method for building a mixture model. Given the desired number of clusters K ≥ 2, we start with a two-component mixture and we optimize the likelihood by repeatedly applying a Split-Merge operation. When an optimum is obtained, we add a new component to the model by splitting in two, a properly chosen cluster. This goes on until the number of components reaches a preset limiting value. We have performed numerical experiments on several data–sets and report a performance comparison with other rival methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, San Diego (1990)
Ripley, B.: Pattern Recognition and Neural Networks. Cambridge Univ. Press Inc., Cambridge, UK (1996)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley-Interscience, New York (2001)
Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Dempster, A., Laird, N., Rubin, D.: Maximum Likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. B 39, 1–38 (1977)
Li, J., Barron, A.: Mixture density estimation. In: Advances in Neural Information Processing Systems, pp. 279–285. The MIT Press, Cambridge, MA (2000)
Vlassis, N., Likas, A.: A greedy EM algorithm for Gaussian mixture learning. Neural Processing Letters 15, 77–87 (2001)
Figueiredo, M., Jain, A.: Unsupervised learning of finite mixture models. IEEE Trans. Pattern Analysis and Machine Intelligence 24(3), 381–396 (2002)
Ueda, N., Nakano, R., Ghahramani, Z., Hinton, G.: SMEM algorithm for mixture models. Neural Computation 12(9), 2109–2128 (2000)
Merz, C., Murphy, P.: UCI repository of machine learning databases.Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Cocosco, C., Kollokian, V., Kwan, R.S., Evans, A.: BrainWeb: Online interface to a 3D MRI simulated brain database. NeuroImage 5(3), 2/4, S425 (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Blekas, K., Lagaris, I.E. (2007). Split–Merge Incremental LEarning (SMILE) of Mixture Models. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-74695-9_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74693-5
Online ISBN: 978-3-540-74695-9
eBook Packages: Computer ScienceComputer Science (R0)