Advertisement

Feature Extraction with Weighted Samples Based on Independent Component Analysis

  • Nojun Kwak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

This study investigates a new method of feature extraction for classification problems with a considerable amount of outliers. The method is a weighted version of our previous work based on the independent component analysis (ICA). In our previous work, ICA was applied to feature extraction for classification problems by including class information in the training. The resulting features contain much information on the class labels producing good classification performances. However, in many real world classification problems, it is hard to get a clean dataset and inherently, there may exist outliers or dubious data to complicate the learning process resulting in higher rates of misclassification. In addition, it is not unusual to find the samples with the same inputs to have different class labels. In this paper, Parzen window is used to estimate the correctness of the class information of a sample and the resulting class information is used for feature extraction.

Keywords

Feature Extraction Linear Discriminant Analysis Independent Component Analysis Class Label Independent Component Analysis 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Joliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (1986)Google Scholar
  2. 2.
    Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7(6) (June 1995)Google Scholar
  3. 3.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)MATHGoogle Scholar
  4. 4.
    Kwak, N., Choi, C.-H.: Feature extraction based on ica for binary classification problems. IEEE Trans. on Knowledge and Data Engineering 15(6), 1374–1388 (2003)CrossRefGoogle Scholar
  5. 5.
    Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Statistics 33, 1065–1076 (1962)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons, Chichester (1991)MATHCrossRefGoogle Scholar
  7. 7.
    Meilhac, C., Nastar, C.: Relevance feedback and catagory search in image databases. In: Proc. IEEE Int’l Conf. on Content-based Access of Video and Image databases, Florence, Italy (June 1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nojun Kwak
    • 1
  1. 1.Samsung Electronics, SuwonSuwon-Si, Gyeonggi-DoKorea

Personalised recommendations