Abstract
Bayesian Ying-Yang (BYY) harmony learning system is a newly developed framework for statistical learning. Via the BYY harmony leaning on finite mixtures, model selection can be made automatically during parameter learning. In this paper, this automated model selection learning mechanism is extended to logarithmic normal (log-normal) mixtures. Actually, an adaptive gradient BYY harmony learning algorithm is proposed for log-normal mixtures. It is demonstrated by the experiments that the proposed BYY harmony learning algorithm not only automatically determines the number of actual log-normal distributions in the sample dataset, but also leads to a satisfactory estimation of the parameters in the original log-normal mixture.
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
Mclachlan, G.J., Peel, D.: Finite mixture Models. John Wiley & Sons, New York (2000)
Akaike, H.: A new look at the statistical model identification. IEEE Trans. on Automatic Control AC-19, 716–723 (1974)
Scharz, G.: Estimating the dimension of a model. The Annals of Statistics 6, 461–464 (1978)
Figueiredo, M.A.T., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(3), 381–395 (2002)
Xu, L.: BYY harmony learning, structural RPCL, and topological self-organizing on mixture modes. Neural Networks 15, 1231–1237 (2002)
Ma, J., Wang, T., Xu, L.: A gradient BYY harmony learning rule on Gaussian mixture with automated model selection. Neurocomputing 56, 481–487 (2004)
Ma, J., Wang, L.: BYY harmony learning on finite mixture: adaptive gradient implementation and a floating RPCL mechanism. Neural Processing Letters 24, 19–40 (2006)
Ma, J., Liu, J.: The BYY annealing learning algorithm for Gaussian mixture with automated model selection. Pattern Recognition 40(7), 2029–2037 (2007)
Ma, J., Liu, J., Ren, Z.: Parameter estimation of Poisson mixture with automated model selection through BYY harmony learning. Pattern Recognition 42(11), 2659–2670 (2009)
Ren, Z., Ma, J.: BYY Harmony Learning on Weibull Mixture with Automated Model Selection. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds.) ISNN 2008, Part I. LNCS, vol. 5263, pp. 589–599. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, Y., Ren, Z., Ma, J. (2013). Automated Model Selection and Parameter Estimation of Log-Normal Mixtures via BYY Harmony Learning. In: Huang, DS., Gupta, P., Wang, L., Gromiha, M. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2013. Communications in Computer and Information Science, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39678-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-39678-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39677-9
Online ISBN: 978-3-642-39678-6
eBook Packages: Computer ScienceComputer Science (R0)