000			03140nam a2200433 c 4500
942			_2lcc _cLE
988			_aSpringer_Engineering_2020
999			_c114382 _d114382 _x1
001			114382
003			ES-MaUEC
005			20230110040219.0
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
008			190823s2020 gw a o |||| 0|eng d
020			_a9783030224561
024	7		_a10.1007/978-3-030-22456-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA278.5 _b2020 EB
100	1		_aTaguchi, Y-h _eautor _9671606
245	1	0	_aUnsupervised feature extraction applied to bioinformatics : _bA PCA based and TD based approach _cby Y-h. Taguchi
250			_aFirst edition
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XVIII, 321 páginas) _b111 ilustraciones, 94 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aUnsupervised and Semi-Supervised Learning _x2522-848X
490	0		_aEngineering (Springer-11647)
505	0		_aIntroduction to linear algebra -- Matrix factorization -- Tensor decompositions -- PCA based unsupervised FE -- TD based unsupervised FE -- Application of PCA/TD based unsupervised FE to bioinformatics -- Application of TD based unsupervised FE to bioinformatics.
520	3		_aThis book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. Allows readers to analyze data sets with small samples and many features; Provides a fast algorithm, based upon linear algebra, to analyze big data; Includes several applications to multi-view data analyses, with a focus on bioinformatics.
650		7	_2embne _aBioinformática _9160489
650		7	_2embne _9139229 _aCálculo tensorial
650		7	_2embne _9667327 _aAnálisis de correspondencias
776	0	8	_iPrinted edition: _z9783030224554
776	0	8	_iPrinted edition: _z9783030224578
776	0	8	_iPrinted edition: _z9783030224585
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-22456-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
998			_aSI _cm _dz _feng _ggw _h0 _b12/2019 _eel _zSI