Feature Extraction and Selection

  • Xiaoxia Yin
  • Brian W.-H. Ng
  • Derek Abbott


One of the tasks of pattern recognition is to convert patterns to features, where these features are a description of the collected data in a compact form. Ideally, these features only contain relevant information, which then play a crucial role in determining the division of properties concerning each class. Mathematical models of feature extraction lead to a dimensionality reduction, resulting in lower-dimensional representation of the information. Following feature extraction, feature selection has an important influence on classification accuracy, necessary time for classification, the number of examples for learning, and the cost of performing classification.


Leaf Node Wavelet Packet Feature Extraction Method ARMA Model Prediction Error Variance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Coifman-R., and Wickerhauser-M. (1992). Entropy-based algorithms for best basis selection, IEEE Transactions on Information Theory, 38(2), p. 713–718.MATHCrossRefGoogle Scholar
  2. Duda-R., and Hart-P. (1973). Pattern Classification and Scene Analysis, 4th edn, John Wiley and Sons Inc, New York, USA.MATHGoogle Scholar
  3. Faure-P. (1976). Stochastic realization algorithms, In System Identification: Advances and Case Studies, Eds. R. K. Mehra and D. G. Larniotis, Academic Press, New York, USA.Google Scholar
  4. Jain-S., and Deshpande-G. (2004). Parametric modeling of brain signals, The Proceeding of IEEE: Technology for Life: North Carolina Symposium on Biotechnology and Bioinformatics, pp. 85–91.Google Scholar
  5. Liang-G., Wilkes-D. M., and Cadzow-J. A. (1993). ARMA model order estimation based on the eigenvalues of the covariance matrix, IEEE Transactions on Signal Processing, 41(10), pp. 3003–3009.MATHCrossRefGoogle Scholar
  6. Proakis-J., and Manolakis-D. (1996). Digital Signal Processing: Principles, Algorithms, and Applications, Prentice-Hall, Inc., NJ, USA.Google Scholar
  7. Therrien-C., and Oppenheim-A. (1992). Discrete Random Signals and Statistical Signal Processing, Prentice Hall, New Jersey, USA.MATHGoogle Scholar
  8. Vetterli-M., and Kovacevic-J. (1995). Wavelets and Subband Coding, Prentice-Hall PTR, New Jersey.MATHGoogle Scholar
  9. Yin-X.-X., Ng-W.-H. B., Ferguson-B., and Abbott-D. (2007a). Application of auto-regressive models and wavelet sub-bands for classifying terahertz pulse measurements, Journal of Biological Systems, 15, pp. 551–571.MATHCrossRefGoogle Scholar
  10. Zhang-Y., Wang-R., and Monroe-K. (1997). Using wavelet network in nonparametric estimation, IEEE Transaction Neural Networks, 8(2), pp. 227–236.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Xiaoxia Yin
    • 1
  • Brian W.-H. Ng
    • 1
  • Derek Abbott
    • 1
  1. 1.School of Electrical and Electronic EngineeringUniversity of AdelaideAdelaideAustralia

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