On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling

  • Addisson Salazar

Part of the Springer Theses book series (Springer Theses, volume 4)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Addisson Salazar
    Pages 1-28
  3. Addisson Salazar
    Pages 29-55
  4. Addisson Salazar
    Pages 83-103
  5. Addisson Salazar
    Pages 105-128
  6. Addisson Salazar
    Pages 173-180
  7. Back Matter
    Pages 181-185

About this book


A natural evolution of statistical signal processing, in connection with the progressive increase in computational power, has been exploiting higher-order information. Thus, high-order spectral analysis and nonlinear adaptive filtering have received the attention of many researchers. One of the most successful techniques for non-linear processing of data with complex non-Gaussian distributions is the independent component analysis mixture modelling (ICAMM). This thesis defines a novel formalism for pattern recognition and classification based on ICAMM, which unifies a certain number of pattern recognition tasks allowing generalization. The versatile and powerful framework developed in this work can deal with data obtained from quite different areas, such as image processing, impact-echo testing, cultural heritage, hypnograms analysis, web-mining and might therefore be employed to solve many different real-world problems.


Classification of Archaeological Ceramics Image Processing Impact-echo Measurements Independent Component Analysis (ICA) Independent Component Analysis Mixture Machine Learning Modelling (ICAMM) Non-parametric Density Estimation PhD Thesis Semi-supervised Learning Statistical Pattern Recognition

Authors and affiliations

  • Addisson Salazar
    • 1
  1. 1.Departamento de ComunicacionesUniversidad Politecnica de ValenciaValenciaSpain

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-30751-5
  • Online ISBN 978-3-642-30752-2
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
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