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In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t

Independent component analysis. --- Machine learning.

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Independent Component Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from linearly mixed data, and belongs to the class of general linear models. ICA provides a better decomposition than other well-known models such as principal component analysis. This self-contained book contains a structured series of edited papers by leading researchers in the field, including an extensive introduction to ICA. The major theoretical bases are reviewed from a modern perspective, current developments are surveyed and many case studies of applications are described in detail. The latter include biomedical examples, signal and image denoising and mobile communications. ICA is discussed in the framework of general linear models, but also in comparison with other paradigms such as neural network and graphical modelling methods. The book is ideal for researchers and graduate students in the field.

Neural networks (Computer science) --- Independent component analysis.

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Independent Component Analysis (ICA) has recently become an important tool for modelling and understanding empirical datasets. It is a method of separating out independent sources from linearly mixed data, and belongs to the class of general linear models. ICA provides a better decomposition than other well-known models such as principal component analysis. This self-contained book contains a structured series of edited papers by leading researchers in the field, including an extensive introduction to ICA. The major theoretical bases are reviewed from a modern perspective, current developments are surveyed and many case studies of applications are described in detail. The latter include biomedical examples, signal and image denoising and mobile communications. ICA is discussed in the framework of general linear models, but also in comparison with other paradigms such as neural network and graphical modelling methods. The book is ideal for researchers and graduate students in the field.

Neural networks (Computer science) --- Multivariate analysis --- Réseaux neuronaux (Informatique) --- Analyse multivariée --- Independent component analysis. --- Multivariate analysis. --- Neural networks (Computer science). --- Réseaux neuronaux (Informatique) --- Analyse multivariée

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The purpose of this lecture book is to present the state of the art in nonlinear blind source separation, in a form appropriate for students, researchers and developers. Source separation deals with the problem of recovering sources that are observed in a mixed condition. When we have little knowledge about the sources and about the mixture process, we speak of blind source separation. Linear blind source separation is a relatively well studied subject. Nonlinear blind source separation is still in a less advanced stage, but has seen several significant developments in the last few years. This publication reviews the main nonlinear separation methods, including the separation of post-nonlinear mixtures, and the MISEP, ensemble learning and kTDSEP methods for generic mixtures. These methods are studied with a significant depth. A historical overview is also presented, mentioning most of the relevant results, on nonlinear blind source separation, that have been presented over the years.

Blind source separation. --- Nonlinear theories. --- Nonlinear problems --- Nonlinearity (Mathematics) --- Blind signal separation --- BSS (Blind source separation) --- Signal processing. --- Source separation. --- Nonlinear blind source separation. --- Independent component analysis. --- Nonlinear ICA. --- Calculus --- Mathematical analysis --- Mathematical physics --- Source separation (Signal processing)

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Neural networks (Computer science) --- Multivariate analysis. --- Réseaux neuronaux (Informatique) --- Analyse multivariée --- Signal processing --- Electronic noise --- Independent component analysis --- Electrical Engineering --- Electrical & Computer Engineering --- Engineering & Applied Sciences --- Digital techniques --- ICA (Independent component analysis) --- Computer science. --- Special purpose computers. --- Coding theory. --- Computers. --- Algorithms. --- Statistics. --- Computer Science. --- Special Purpose and Application-Based Systems. --- Algorithm Analysis and Problem Complexity. --- Computation by Abstract Devices. --- Coding and Information Theory. --- Statistics and Computing/Statistics Programs. --- Signal, Image and Speech Processing. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Algorism --- Algebra --- Arithmetic --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Machine theory --- Calculators --- Cyberspace --- Data compression (Telecommunication) --- Digital electronics --- Information theory --- Signal theory (Telecommunication) --- Computer programming --- Special purpose computers --- Computers --- Informatics --- Science --- Foundations --- Multivariate analysis --- Software engineering. --- Computer software. --- Mathematical statistics. --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Software, Computer --- Computer software engineering --- Engineering --- Information theory. --- Statistics . --- Signal processing. --- Image processing. --- Speech processing systems. --- Computational linguistics --- Electronic systems --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Processing, Signal --- Information measurement --- Communication theory --- Communication