Includes bibliographical references and index.

PART I. Theory basics of BSS -- Mathematical foundation of blind source separation -- General model and classical algorithm for BSS -- Evaluation criteria for the bss algorithm -- PART II. Independent component analysis -- Independent component analysis -- Fast independent component analysis and its application -- Maximum likelihood independent component analysis and its application -- Overcomplete independent component analysis algorithms and applications -- Kernel independent component analysis -- Non-negative independent component analysis and its application -- Constraint independent component analysis algorithms and applications -- Optimized independent component analysis algorithms and applications -- Supervised learning independent component analysis algorithms and applications -- PART III. Advances and applications of BSS -- Non-negative matrix factorization algorithms and applications -- Sparse component analysis and applications -- Glossary.

"This book offers a general overview of the basics of Blind Source Separation, important solutions and algorithms, and in-depth coverage of applications in image feature extraction, remote sensing image fusion, mixed-pixel decomposition of SAR images, image object recognition fMRI medical image processing, geochemical and geophysical data mining, mineral resources prediction and geoanomalies information recognition. Firstly, the background and theory basics of blind source separation are introduced, which provides the foundation for the following work. Matrix operation, foundations of probability theory and information theory basics are included here. There follows the fundamental mathematical model and fairly new but relatively established blind source separation algorithms, such as Independent Component Analysis (ICA) and its improved algorithms (Fast ICA, Maximum Likelihood ICA, Overcomplete ICA, Kernel ICA, Flexible ICA, Non-negative ICA, Constrained ICA, Optimised ICA). The last part of the book considers the very recent algorithms in BSS e.g. Sparse Component Analysis (SCA) and Non-negative Matrix Factorization (NMF). Meanwhile, in-depth cases are presented for each algorithm in order to help the reader understand the algorithm and its application field"--Provided by publisher.

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