On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
Author:   Addisson Salazar
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Edition:   2013 ed.
Volume:   4
ISBN:  

9783642428753


Pages:   186
Publication Date:   09 August 2014
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling


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Overview

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.

Full Product Details

Author:   Addisson Salazar
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   2013 ed.
Volume:   4
Dimensions:   Width: 15.50cm , Height: 1.10cm , Length: 23.50cm
Weight:   3.226kg
ISBN:  

9783642428753


ISBN 10:   3642428754
Pages:   186
Publication Date:   09 August 2014
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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