Abstract and Applied Analysis

Independent Component Analysis Based on Information Bottleneck

Qiao Ke, Jiangshe Zhang, H. M. Srivastava, Wei Wei, and Guang-Sheng Chen

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text


The paper is mainly used to provide the equivalence of two algorithms of independent component analysis (ICA) based on the information bottleneck (IB). In the viewpoint of information theory, we attempt to explain the two classical algorithms of ICA by information bottleneck. Furthermore, via the numerical experiments with the synthetic data, sonic data, and image, ICA is proved to be an edificatory way to solve BSS successfully relying on the information theory. Finally, two realistic numerical experiments are conducted via FastICA in order to illustrate the efficiency and practicality of the algorithm as well as the drawbacks in the process of the recovery images the mixing images.

Article information

Abstr. Appl. Anal., Volume 2015 (2015), Article ID 386201, 8 pages.

First available in Project Euclid: 17 August 2015

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier


Ke, Qiao; Zhang, Jiangshe; Srivastava, H. M.; Wei, Wei; Chen, Guang-Sheng. Independent Component Analysis Based on Information Bottleneck. Abstr. Appl. Anal. 2015 (2015), Article ID 386201, 8 pages. doi:10.1155/2015/386201. https://projecteuclid.org/euclid.aaa/1439816222

Export citation


  • E. Gokcay and J. C. Principe, “Information theoretic clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 2, pp. 158–171, 2002.
  • A. Kraskov, H. Stögbauer, R. G. Andrzejak et al., “Hierarchical clustering using mutual information,” Europhysics Letters, vol. 70, no. 2, pp. 278–284, 2005.
  • A. J. Bell and T. J. Sejnowski, “An information-maximization approach to blind separation and blind deconvolution,” Neural Computation, vol. 7, no. 6, pp. 1129–1159, 1995.
  • A. Hyvärinen, J. Hurri, and P. O. Hoyer, Natural Image Statistics: A Probabilistic Approach to Early Computational Vision, Springer, 2009.
  • T. M. Cover and J. A. Thomas, Elements of Information Theory, John Wiley & Sons, 2012.
  • H. Zhang, X. Liu, J. Wang et al., “Robust ${H}_{\infty }$ sliding mode control with pole placement for a fluid power electrohydraulic actuator (EHA) system,” The International Journal of Advanced Manufacturing Technology, vol. 73, no. 5-8, pp. 1095–1104, 2014.
  • H. Zhang, Y. Shi, and J. Wang, “On energy-to-peak filtering for nonuniformly sampled nonlinear systems: a Markovian jump system approach,” IEEE Transactions on Fuzzy Systems, vol. 22, no. 1, pp. 212–222, 2014.
  • H. Zhang and J. Wang, “Combined feedback-feedforward tracking control for networked control systems with probabilistic delays,” Journal of the Franklin Institute, vol. 351, no. 6, pp. 3477–3489, 2014.
  • A. Hyvärinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Networks, vol. 13, no. 4-5, pp. 411–430, 2000.
  • H. Zhang, X. Zhang, and J. Wang, “Robust gain-scheduling energy-to-peak control of vehicle lateral dynamics stabilisation,” Vehicle System Dynamics, vol. 52, no. 3, pp. 309–340, 2014.
  • W. Wei and Y. Qi, “Information potential fields navigation in wireless Ad-Hoc sensor networks,” Sensors, vol. 11, no. 5, pp. 4794–4807, 2011.
  • W. Wei, P. Shen, Y. Zhang, and L. Zhang, “Information fields navigation with piece-wise polynomial approximation for high-performance OFDM in WSNs,” Mathematical Problems in Engineering, vol. 2013, Article ID 901509, 9 pages, 2013.
  • Z. Shuai, H. Zhang, J. Wang et al., “Lateral motion control for four-wheel-independent-drive electric vehicles using optimal torque allocation and dynamic message priority scheduling,” Control Engineering Practice, vol. 24, pp. 55–66, 2014.
  • N. Tishby, F. C. Pereira, and W. Bialek, “The information Bottleneck method,” in Proceedings of the 37th annual Allerton Conference on Communication, Control, and Computing, pp. 368–377, September 1999.
  • J. Rice, Mathematical Statistics and Data Analysis, Cengage Learning, 2006. \endinput