On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
(Sprache: Englisch)
This outstanding review of the literature on the core theoretical foundations of applied statistical pattern recognition defines a novel mode of pattern recognition and classification, based on independent component analysis mixture modeling (ICAMM).
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This outstanding review of the literature on the core theoretical foundations of applied statistical pattern recognition defines a novel mode of pattern recognition and classification, based on independent component analysis mixture modeling (ICAMM).
Klappentext zu „On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling “
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.
Inhaltsverzeichnis zu „On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling “
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.
Bibliographische Angaben
- Autor: Addisson Salazar
- 2014, 2013, XXII, 186 Seiten, Masse: 16,8 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3642428754
- ISBN-13: 9783642428753
Sprache:
Englisch
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