Skip to main content
SpringerLink
Log in

An evolving neural network to perform dynamic principal component analysis

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Nonlinear principal component analysis is one of the best dimension reduction techniques developed during the recent years which have been applied in different signal-processing applications. In this paper, an evolving category of auto-associative neural network is presented which is applied to perform dynamic nonlinear principal component analysis. Training strategy of the network implements both constructive and destructive algorithms to extract dynamic principal components of speech database. In addition, the proposed network makes it possible to eliminate some dimensions of sequences that do not play important role in the quality of speech processing. Finally, the network is successfully applied to solve missing data problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Oja E (1982) A simplified neuron model as a principal component analyzer. J Math Biol 15:267–273

    Article  MATH  MathSciNet  Google Scholar 

  2. Saegusa R, Sakano H, Hashimoto S (2004) Nonlinear principal component analysis to preserve the order of principal components. Neurocomputing 61:57–70

    Article  Google Scholar 

  3. Makki B, Salehi SA, Sadati N, Noori Hosseini M (2007) Voice conversion using nonlinear principal component analysis. In: IEEE symposium on computational intelligence in image and signal processing, pp 336–339

  4. Linting M, Meulman JJ, Groenen PJF, van der Kooij AJ (2007) Nonlinear principal components analysis: Introduction and application. Psychol Methods 12(3):336–358

    Article  Google Scholar 

  5. Kramer MA (1991) Nonlinear principal component analysis using auto associative neural networks. AIChE J 37:233–243

    Article  Google Scholar 

  6. Bar-Gad I, Morris G, Bergman H (2003) Information processing, dimensionality reduction and reinforcement learning in the basal ganglia. Prog Neurobiol 71(6):439–473

    Article  Google Scholar 

  7. Xin Y (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423–1447

    Article  Google Scholar 

  8. Makki B, Salehi SA, Noori Hosseini M, Sadati N (2007) Principal component analysis using constructive neural networks. In: International joint conference on neural networks, pp 558–562

  9. Rios A, Kabuka M (1995) Image compression with a dynamic auto associative neural network. Math Comp Model 21(1–2):159–171

    Article  Google Scholar 

  10. Ning GX, Leung SH, Chu KK, Wei G (2006) A dynamic parameter compensation method for noisy speech recognition. Speech Commun 48(10):1283–1293

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Amirkabir University of Technology, #15, Dadafarin St., Namazzade St., Taslimi St., Dibaji-jonubi Ave., P.O. Box: 1959673965, Tehran, Iran

    Behrooz Makki, Mona Noori Hosseini & Seyyed Ali Seyyedsalehi

Authors
  1. Behrooz Makki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mona Noori Hosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seyyed Ali Seyyedsalehi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Behrooz Makki.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Makki, B., Noori Hosseini, M. & Seyyedsalehi, S.A. An evolving neural network to perform dynamic principal component analysis. Neural Comput & Applic 19, 459–463 (2010). https://doi.org/10.1007/s00521-009-0328-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-009-0328-1

Keywords

Search

Navigation