Download Blind Source Separation: Theory and Applications by Xianchuan Yu PDF

By Xianchuan Yu

A systematic exploration of either vintage and modern algorithms in blind resource separation with functional case studies   

The booklet provides an outline of Blind resource Separation, a comparatively new sign processing method.  as a result of the multidisciplinary nature of the topic, the ebook has been written with a purpose to attract an viewers from very assorted backgrounds. simple mathematical talents (e.g. on matrix algebra and foundations of chance thought) are crucial which will comprehend the algorithms, even supposing the ebook is written in an introductory, obtainable style.

This e-book deals a normal assessment of the fundamentals of Blind resource Separation, vital ideas and algorithms, and in-depth insurance of purposes in picture function extraction, distant sensing photograph fusion, mixed-pixel decomposition of SAR pictures, photo item popularity fMRI scientific photo processing, geochemical and geophysical information mining, mineral assets prediction and geoanomalies details popularity. to start with, the history and thought fundamentals of blind resource separation are brought, which gives the basis for the subsequent paintings. Matrix operation, foundations of likelihood conception and knowledge idea fundamentals are integrated right here. There follows the basic mathematical version and reasonably new yet really confirmed blind resource separation algorithms, resembling self reliant part research (ICA) and its more desirable algorithms (Fast ICA, greatest chance ICA, Overcomplete ICA, Kernel ICA, versatile ICA, Non-negative ICA, restricted ICA, Optimised ICA). The final a part of the ebook considers the very fresh algorithms in BSS e.g. Sparse part research (SCA) and Non-negative Matrix Factorization (NMF). in the meantime, in-depth instances are awarded for every set of rules with a purpose to support the reader comprehend the set of rules and its program field.

  • A systematic exploration of either vintage and modern algorithms in blind resource separation with functional case studies
  • Presents new better algorithms geared toward various purposes, equivalent to picture characteristic extraction, distant sensing picture fusion, mixed-pixel decomposition of SAR photographs, snapshot item acceptance, and MRI scientific photograph processing
  • With functions in geochemical and geophysical information mining, mineral assets prediction and geoanomalies info recognition
  • Written via a professional group with authorised thoughts in blind resource separation and its purposes in average science
  • Accompanying web site features a software program process supplying codes for many of the algorithms pointed out within the booklet, bettering the training experience

Essential interpreting for postgraduate scholars and researchers engaged within the sector of sign processing, information mining, photo processing and popularity, details, geosciences, lifestyles sciences.

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Extra resources for Blind Source Separation: Theory and Applications

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2. Maximum entropy Since entropy can be interpreted as a measure of randomness, the maximum entropy density is the most random of all probability density functions satisfying the constraints. 136) Now, consider the set of random variables with zero mean and fixed variance, which can take all the values on the real line. The maximum entropy distribution for such variables, with two constraints, is the Gaussian distribution. 137) p(???? ) = A exp(a1 ???? 2 + a2 ???? ) A Gaussian variable has the largest entropy of all random variables with unit variance.

1998) Natural gradient works efficiently in learning. Neural Computation, 10(2), 251–276. , Yang, H. (1996) A new learning algorithm for blind source separation, in Advances in Neural Information Processing System, MIT Press, Cambridge, MA, pp. 757–763. J. (1998) Independent component representations for face recognition. Proceedings of the SPIE: Conference on Human Vision and Electronic Imaging, pp. 528–539. J. (2002) Face recognition by independent component analysis. IEEE Transactions on Neural Networks, 13(6), 1450–1464.

If the mean is known together with a statistical description of a Gaussian process, the estimation is complete. If the statistical description is nonlinear and deviates from the Gaussian, we need to obtain the information from higher-order statistics. Higher-order statistics generally include higher-order moments, higher-order cumulants, and their spectra, namely, the higher-order moment spectrum, higher-order cumulant spectrum (also called the polyspectrum) and inverted higher-order cumulant spectrum (also called the inverted multi-spectrum).

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