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).

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.

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

Inhalt
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.