Abstract
Gaussian Mixture Models are among the most statistically mature methods which are used to make statistical inferences as well as performing unsupervised clustering. Formally, a gaussian mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the data set. In this paper, a probabilistic clustering based on the finite mixture models of the data distribution is suggested. An important issue in the finite mixture model-based clustering approach is to select the number of mixture components of clusters. In this sense, we focus on statistical inference for finite mixture models and illustrate how the variational Bayesian approach can be used to determine a suitable number of components in the case of a mixture of Gaussian distributions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hand D, Mannila H, Smyth P (2001) Principles of data mining. MIT Press, Cambridge
Yu Guoshen (2012) Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity. IEEE Trans Image Process 21(5):2481–2499
Yin J, Zhang Y, Gao L (2012) Accelerating expectation-maximization algorithms with frequent updates. In: Proceedings of the IEEE international conference on cluster computing
Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press, Cambridge, pp 151–152
Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin
Fox C, Roberts S (2012) A tutorial on variational Bayes. Artif Intell Rev 38(2):85–95
Takekawa Takashi, Fukai Tomoki (2009) A novel view of the variational Bayesian clustering. Neuro Comput 72:3366–3369
Attias H (2000) A variational Bayesian framework for graphical models. In: Leen T et al (eds) Advances in neural information processing systems, vol 12. MIT Press, Cambridge
Mackay et al (2003) Model comparison and Occam’s Razor. Cambridge University Press, Cambridge
Sijbers J, den Dekker AJ (2004) Maximum likelihood estimation of signal amplitude and noise variance from MR data. Magn Reson Med 51(3):586–594
Roche A, Ribes D, Bach-Cuadra M, Kruger G (2011) On the convergence of EM-like algorithms for image segmentation using Markov random fields. Med Image Anal 15:830–839
Acknowledgment
This research was supported by the Ministry of Science and Technology under contract numbers MOST 103-2221-E-212-011 and MOST 104-2221-E-212-011.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Chen, MS., Wang, HF., Hwang, CP., Ho, TY., Hung, CH. (2016). A Variational Bayesian Approach for Unsupervised Clustering. In: Hung, J., Yen, N., Li, KC. (eds) Frontier Computing. Lecture Notes in Electrical Engineering, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-10-0539-8_63
Download citation
DOI: https://doi.org/10.1007/978-981-10-0539-8_63
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0538-1
Online ISBN: 978-981-10-0539-8
eBook Packages: Computer ScienceComputer Science (R0)