TY  - BOOK
AU  - Salazar,Addisson
ED  - SpringerLink (Online service)
TI  - On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
T2  - Springer Theses, Recognizing Outstanding Ph.D. Research,
SN  - 9783642307522
AV  - TK5102.9 
U1  - 621.382 23
PY  - 2013///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg, Imprint: Springer
KW  - Engineering
KW  - Pattern recognition
KW  - Complexity, Computational
KW  - Signal, Image and Speech Processing
KW  - Pattern Recognition
KW  - Complexity
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/978-3-642-30752-2
ER  -