On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling

2013
ISBN: 978-3-642-42875-3
Verlag: Springer

Buch, Englisch, Band 4, 186 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 3226 g

Reihe: Springer Theses

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.

Zielgruppe

Research

Autoren/Hrsg.

Salazar, Addisson

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Introduction.-
ICA and ICAMM Methods.-
Learning Mixtures of Independent Component Analysers.-
Hierarchical Clustering from ICA Mixtures.-
Application of ICAMM to Impact-Echo Testing.-
Cultural Heritage Applications: Archaeological Ceramics and Building Restoration.-
Other Applications: Sequential Dependence Modelling and Data Mining.-
Conclusions.