Skip to main content
SpringerLink
Log in

On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling

  • Book
  • © 2013

Overview

Authors:
  1. Addisson Salazar

    1. Departamento de Comunicaciones, Universidad Politecnica de Valencia, Valencia, Spain

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Nominated as an outstanding PhD theses by the Polytechnic University of Valencia
  • Present an excellent state-of-the-art literature review of the main applied theoretical foundations of statistical pattern recognition
  • Gives new insights into independent component analysis (ICA) and independent component analysis mixture modelling (ICAMM) research in the context of statistical pattern recognition
  • Defines a novel general framework in statistical pattern recognition based on independent component analysis mixture modeling
  • Includes supplementary material: sn.pub/extras

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

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

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (8 chapters)

  1. Front Matter

    Pages i-xxii
    Download chapter PDF

  2. Introduction

    • Addisson Salazar
    Pages 1-28

  3. ICA and ICAMM Methods

    • Addisson Salazar
    Pages 29-55

  4. Learning Mixtures of Independent Component Analysers

    • Addisson Salazar
    Pages 57-82

  5. Hierarchical Clustering from ICA Mixtures

    • Addisson Salazar
    Pages 83-103

  6. Application of ICAMM to Impact-Echo Testing

    • Addisson Salazar
    Pages 105-128

  7. Cultural Heritage Applications: Archaeological Ceramics and Building Restoration

    • Addisson Salazar
    Pages 129-153

  8. Other Applications: Sequential Dependence Modelling and Data Mining

    • Addisson Salazar
    Pages 155-172

  9. Conclusions

    • Addisson Salazar
    Pages 173-180

  10. Back Matter

    Pages 181-185
    Download chapter PDF
Back to top

Keywords

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.

Authors and Affiliations

  • Departamento de Comunicaciones, Universidad Politecnica de Valencia, Valencia, Spain

    Addisson Salazar

Bibliographic Information

  • Book Title: On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling

  • Authors: Addisson Salazar

  • Series Title: Springer Theses

  • DOI: https://doi.org/10.1007/978-3-642-30752-2

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2013

  • Hardcover ISBN: 978-3-642-30751-5Published: 20 July 2012

  • Softcover ISBN: 978-3-642-42875-3Published: 09 August 2014

  • eBook ISBN: 978-3-642-30752-2Published: 20 July 2012

  • Series ISSN: 2190-5053

  • Series E-ISSN: 2190-5061

  • Edition Number: 1

  • Number of Pages: XXII, 186

  • Topics: Signal, Image and Speech Processing, Pattern Recognition, Complexity

Publish with us

Policies and ethics

Back to top

Search

Navigation