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.
| ISBN: | 9783642428753 |
| Publication date: | 9th August 2014 |
| Author: | Addisson Salazar |
| Publisher: | Springer an imprint of Springer Berlin Heidelberg |
| Format: | Paperback |
| Pagination: | 186 pages |
| Series: | Springer Theses |
| Genres: |
Electronics engineering Pattern recognition Cybernetics and systems theory Maths for engineers Digital signal processing (DSP) |
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.
On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling features in the following genres: Electronics engineering, Pattern recognition, Cybernetics and systems theory, Maths for engineers, Digital signal processing (DSP)
On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling is available in Paperback, Hardback
On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling was written by Addisson Salazar and published by Springer an imprint of Springer Berlin Heidelberg
On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling has 186 pages
Yes it is part of Springer Theses series