000			01901 a2200229 4500
003			Nust
005			20170207154017.0
020			_a0-471-22131-7
020			_a978-0-471-22131-9
040			_cNust
082	1		_a519.5354
100	1		_aHyv䲩nen, Aapo
245	0	0	_aIndependent component analysis-(E-BOOK) _h[Elektronisk resurs] _cAapo Hyv䲩nen, Juha Karhunen, Erkki Oja
260			_aNew York : _bWiley, _c2002
300			_aPDF-fil (xxi, 481 s.)
440		0	_aAdaptive and learning systems for signal processing, communications and control,
505			_a Introduction (Page-1) Part I MATHEMATICAL PRELIMINARIES, 2 Random Vectors and Independence (Page-15) 3 Gradients and Optimization Methods (Page-57) 4 Estimation Theory (Page-77) 5 Information Theory (page-105) 6 Principal Component Analysis and Whitening (page-125), Part II BASIC INDEPENDENT COMPONENT ANALYSIS, 7 What is Independent Component Analysis? (Page-147) 8 ICA by Maximization of Nongaussianity (page-165) 9 ICA by Maximum Likelihood Estimation (page-203) 10 ICA by Minimization of Mutual Information (pag-221) 11 ICA by Tensorial Methods (page-229) 12 ICA by Nonlinear Decorrelation and Nonlinear PCA (page-239) 13 Practical Considerations (page-263) 14 Overview and Comparison of Basic ICA Methods(Page-273) Part III EXTENSIONS AND RELATED METHODS, 15 Noisy ICA (page-293) 16 ICA with Overcomplete Bases (Page-305) 17 Nonlinear ICA (page-315) 18 Methods using Time Structure (page-341) 19 Convolutive Mixtures and Blind Deconvolution (page-355) 20 Other Extensions (page-371) Part IV APPLICATIONS OF ICA, 21 Feature Extraction by ICA (page-391) 22 Brain Imaging Applications (page-407) 23 Telecommunications (page-417) 24 Other Applications (Page-441)
650		7	_aPrincipal components analysis
700	1		_aKarhunen, Juha
700	1		_aOja, Erkki
856	4	0	_uhttp://www3.interscience.wiley.com/cgi-bin/booktoc?ID=93520448
942			_2ddc _cBK
999			_c191468 _d191468