000			03078nam a22003498i 4500
001			CR9780511624148
003			UkCbUP
005			20200124160309.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			090916s2001||||enk o ||1 0|eng|d
020			_a9780511624148 (ebook)
020			_z9780521792981 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA76.87 _b.I516 2001
082	0	0	_a006.3/2 _221
245	0	0	_aIndependent component analysis : _bprinciples and practice / _cedited by Stephen Roberts, Richard Everson.
264		1	_aCambridge : _bCambridge University Press, _c2001.
300			_a1 online resource (xii, 338 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	0		_aFast ICA by a fixed-point algorithm that maximizes non-gaussianity / Aapo Hyvarinen -- ICA, graphical models and variational methods / H. Attias -- Nonlinear ICA / J. Karhunen -- Separation of non-stationary natural signals / Lucas C. Parra & Clay D. Spence -- Separation of non-stationary sources / Jean-Francois Cardoso & Dinh-Tuan Pham -- Blind source separation by sparse decomposition in a signal dictionary / M. Zibulevsky, B.A. Pearlmutter, P. Bofill & P. Kisilev -- Ensemble learning for blind source separation / J.W. Miskin & D.J.C. MacKay -- Image processing methods using ICA mixture models / T.-W. Lee & M.S. Lewicki -- Latent class and trait models for data classification and visualisation / M.A. Girolami -- Particle filters for non-stationary ICA / R.M. Everson & S.J. Roberts -- ICA / W.D. Penny, S.J. Roberts & R.M. Everson.
520			_aIndependent 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.
650		0	_aNeural networks (Computer science)
650		0	_aIndependent component analysis.
700	1		_aRoberts, Stephen, _d1965- _eeditor.
700	1		_aEverson, Richard, _d1961- _eeditor.
776	0	8	_iPrint version: _z9780521792981
856	4	0	_uhttps://doi.org/10.1017/CBO9780511624148
999			_c520938 _d520936