Advertisement

Gradient-Based Swarm Optimization for ICA

  • Rasmikanta PatiEmail author
  • Vikas Kumar
  • Arun K. Pujari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 713)

Abstract

Blind source separation (BSS) is one of the most interesting research problems in signal processing. There are different methods for BSS such as principal component analysis (PCA), independent component analysis (ICA), and singular value decomposition (SVD). ICA is a generative model of determining a linear transformation of the observed random vector to another vector in which the transformed components are statistically independent. Computationally, ICA is formulated as an optimization problem of contrast function, and different algorithms for ICA differ among themselves on the way the contrast function is modeled. Several optimization techniques such as gradient descent and variants, fixed-point iterative methods are employed to optimize the contrast function which is nonlinear, and hence, determining global optimizing point is most often impractical. In this paper, we propose a novel gradient-based particle swarm optimization (PSO) method for ICA in which the gradient information along with the traditional velocity in swarm search is combined to optimize the contrast function. We show empirically that, in this process, we achieve better BSS. The paper focuses on the extraction of one by one source signal like deflation process.

Keywords

ICA Contrast function Optimization Gradient Particle swarm optimization 

References

  1. 1.
    Castella, M., Moreau, E.: A new method for kurtosis maximization and source separation. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 2670–2673. IEEE (2010)Google Scholar
  2. 2.
    Kawamoto, M., Kohno, K., Inouye, Y.: Eigenvector algorithms incorporated with reference systems for solving blind deconvolution of mimo-iir linear systems. IEEE Signal Process. Lett. 14(12), 996–999 (2007)CrossRefGoogle Scholar
  3. 3.
    Castella, M., Moreau, E.: New kurtosis optimization schemes for miso equalization. IEEE Trans. Signal Process. 60(3), 1319–1330 (2012)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Kennedy, J., Eberhart, R.: Particle swarm optimization (pso). In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)Google Scholar
  5. 5.
    Parsopoulos, K.E., Vrahatis, M.N.: Recent approaches to global optimization problems through particle swarm optimization. Nat. Comput. 1(2–3), 235–306 (2002)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Igual, J., Ababneh, J., Llinares, R., Miro-Borras, J., Zarzoso, V.: Solving independent component analysis contrast functions with particle swarm optimization. Artif. Neural Netw. ICANN 2010, 519–524 (2010)Google Scholar
  7. 7.
    Simon, C., Loubaton, P., Jutten, C.: Separation of a class of convolutive mixtures: a contrast function approach. Signal Process. 81(4), 883–887 (2001)CrossRefGoogle Scholar
  8. 8.
    Tugnait, J.K.: Identification and deconvolution of multichannel linear non-gaussian processes using higher order statistics and inverse filter criteria. IEEE Trans. Signal Process. 45(3), 658–672 (1997)CrossRefGoogle Scholar
  9. 9.
    Castella, M., Rhioui, S., Moreau, E., Pesquet, J.C.: Quadratic higher order criteria for iterative blind separation of a mimo convolutive mixture of sources. IEEE Trans. Signal Process. 55(1), 218–232 (2007)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Boscolo, R., Pan, H., Roychowdhury, V.P.: Independent component analysis based on nonparametric density estimation. IEEE Trans. Neural Netw. 15(1), 55–65 (2004)CrossRefGoogle Scholar
  11. 11.
    Haykin, S.S.: Unsupervised Adaptive Filtering: Blind Source Separation, vol. 1. Wiley-Interscience (2000)Google Scholar
  12. 12.
    Vrins, F., Archambeau, C., Verleysen, M.: Entropy minima and distribution structural modifications in blind separation of multimodal sources. In: AIP Conference Proceedings, vol. 735, pp. 589–596. AIP (2004)Google Scholar
  13. 13.
    Krusienski, D.J., Jenkins, W.K.: Nonparametric density estimation based independent component analysis via particle swarm optimization. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Proceedings.(ICASSP’05), vol. 4, pp. iv–357. IEEE (2005)Google Scholar
  14. 14.
    Li, H., Li, Z., Li, H.: A blind source separation algorithm based on dynamic niching particle swarm optimization. In: MATEC Web of Conferences. Volume 61., EDP Sciences (2016)Google Scholar
  15. 15.
    Borowska, B., Nadolski, S.: Particle swarm optimization: the gradient correction. (2009)Google Scholar
  16. 16.
    Noel, M.M., Jannett, T.C.: Simulation of a new hybrid particle swarm optimization algorithm. In: Theory, System (ed.) 2004, pp. 150–153. IEEE, Proceedings of the Thirty-Sixth Southeastern Symposium on (2004)Google Scholar
  17. 17.
    Vesterstrom, J.S., Riget, J., Krink, T.: Division of labor in particle swarm optimisation. In: Evolutionary Computation, 2002. CEC’02. Proceedings of the 2002 Congress on. Volume 2., IEEE (2002) 1570–1575Google Scholar
  18. 18.
    Szabo, D.: A study of gradient based particle swarm optimisers. PhD thesis, Masters thesis, Faculty of Engineering, Built Environment and Information Technology University of Pretoria, Pretoria, South Africa (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Rasmikanta Pati
    • 1
    Email author
  • Vikas Kumar
    • 2
  • Arun K. Pujari
    • 2
    • 3
  1. 1.SUIITSambalpur UniversitySambalpurIndia
  2. 2.School of CISUniversity of HyderabadHyderabadIndia
  3. 3.Central University of RajasthanKishangar, AjmerIndia

Personalised recommendations