Model Selection Using Gaussian Mixture Models and Parallel Computing

By Tian Qiu1, Yiyi Chen1

1. Purdue University

Model Selection Using Gaussian Mixture Models

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Version 1.0 - published on 27 Jul 2016

doi:10.4231/D38C9R53M cite this

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Abstract

In recent years, model selection methods have seen significant advancement, but improvements have tended to be bench marked on its efficiency.  An effective model selection system requires a robust feature extraction module. A model selection system is developed by using Finite Multivariate Generalized Gaussian Mixture Model, which organize data points to clusters. Clustering is basically to assign data set into different groups based on their similarity. In this model, expectation maximization method is used to calculate the distance from each point to their dummy center point, where center point will be changing with the process of simulation to get the best fitting results. Parallel computing is utilized to accelerate simulation process. The performance of the developed model is studied through experimental evaluation with ten thousands data points and identification accuracy. The system still can be improved by a new algorithm to separate the cluster. Performance evaluations will be investigated and compared.

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Credits

I appreciate that professor Guang Lin gives me a great opportunity and Dr. Georgios Karagiannis provides crucial suggestions that helps me a lot. Also, thanks for previous work done by Yiyi Chen and Xuan Liu.

Sponsored by

National Science Foundation

References

  1. Fort, H., & Mungan, M. (2015). Using Expectation Maximization and Resource Overlap Techniques to Classify Species According to Their Niche Similarities in Mutualistic Networks. Entropy, 17(11), 7680-7697. doi:10.3390/e17117680
  2. Gu, F., Zhang, H., Wang, W., & Zhu, D. (2013). PARAFAC-Based Blind Identification of Underdetermined Mixtures Using Gaussian Mixture Model. Circuits Syst Signal Process Circuits, Systems, and Signal Processing, 33(6), 1841-1857. doi:10.1007/s00034-013-9719-8
  3. Jyothi, K., Sailaja, D., & Rao, D. S. (2011). Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model with Distant Microphone Speech. International Journal of Computer Applications IJCA, 14(4), 5-9. doi:10.5120/1835-2463
  4. Li, R., Prasad, V., & Huang, B. (2016). Gaussian Mixture Model-Based Ensemble Kalman Filtering for State and Parameter Estimation for a PMMA Process. Processes, 4(2), 9. doi:10.3390/pr4020009
  5. Xiao, W., Zheng, J., Hua, J., & Zhan, E. (2011). Speaker identification based on Classification Sub-space Gaussian Mixture Model. 2011 International Conference on Image Analysis and Signal Processing. doi:10.1109/iasp.2011.6109116
  6. Huang, T., Peng, H., & Zhang, K. (n.d.). Model Selection for Gaussian Mixture Models.
  7. Chris Mccormick, Gaussian Mixture Models Tutorial And Matlab Code, August 4, 2014, retrieved from https://chrisjmccormick.wordpress.com/2014/08/04/gaussian-mixture- models-tutorial-and-matlab-code.
  8. Tao Huang, Heng Peng and Kun Zhang, Model Selection for Gaussian Mixture Models, January 17, 2013.
  9. Christopher M. Brishop, Mixtures of Gaussians, Pattern Recognition and Machine Learning, Vol 8, 2009, pp. 110-113.
  10.  Figueiredo, Mario A.T. , Jain, A.K, Unsupervised learning of finite mixture models, IEEE Transactions on pattern analysis and machine intelligence, Vol.24, NO. 3, March 2002.
  11.  Ryan M. Reynolds (2002), A Software Based Speaker identification System using Gaussian Mixture Model Classification.
  12. Chen, M. (n.d.). Variational Bayesian Inference for Gaussian Mixture Model - File Exchange - MATLAB Central. Retrieved July 07, 2016, from http://cn.mathworks.com/matlabcentral/fileexchange/35362-variational-bayesian-inference-for-gaussian-mixture-model
  13. Scalability Definition | Investopedia. (2007). Retrieved July 07, 2016, from http://www.investopedia.com/terms/s/scalability.asp

Cite this work

Researchers should cite this work as follows:

  • Tian Qiu; Yiyi Chen (2016), "Model Selection Using Gaussian Mixture Models and Parallel Computing," http://nanohub.org/resources/msugmmpc. (DOI: 10.4231/D38C9R53M).

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