On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling

✍ Scribed by Addisson Salazar (auth.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2013
• Tongue: English
• Leaves: 199
• Series: Springer Theses 4
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xxii
Introduction....Pages 1-28
ICA and ICAMM Methods....Pages 29-55
Learning Mixtures of Independent Component Analysers....Pages 57-82
Hierarchical Clustering from ICA Mixtures....Pages 83-103
Application of ICAMM to Impact-Echo Testing....Pages 105-128
Cultural Heritage Applications: Archaeological Ceramics and Building Restoration....Pages 129-153
Other Applications: Sequential Dependence Modelling and Data Mining....Pages 155-172
Conclusions....Pages 173-180
Back Matter....Pages 181-185

✦ Subjects

Signal, Image and Speech Processing;Pattern Recognition;Complexity