Central Library

Normal view MARC view ISBD view

On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling [electronic resource] / by Addisson Salazar.

By: Salazar, Addisson [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Springer Theses, Recognizing Outstanding Ph.D. Research: 4Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XXII, 186 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642307522.Subject(s): Engineering | Pattern recognition | Complexity, Computational | Engineering | Signal, Image and Speech Processing | Pattern Recognition | ComplexityAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
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.
In: Springer eBooksSummary: 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.
    average rating: 0.0 (0 votes)
No physical items for this record

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.

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.

There are no comments for this item.

Log in to your account to post a comment.