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008			131227s2014 flua ob 001 0 eng d
020			_a9781466556683 (ebook : PDF)
040			_aBD-DhSAU _beng _cBD-DhSAU _erda
090			_aQA278.5 _b.T35 2014
092			_a519.5/35 _bT136
100	1		_aTakane, Yoshio, _eauthor.
245	1	0	_aConstrained principal component analysis and related techniques / _cYoshio Takane, Professor Emeritus, McGill University Montreal, Quebec, Canada and Adjunct Professor at University of Victoria British Columbia, Canada.
264		1	_aBoca Raton : _bChapman and Hall/CRC, _c2014.
264		4	_c�2014
300			_a1 online resource : _btext file, PDF
336			_atext _2rdacontent
337			_acomputer _2rdamedia
338			_aonline resource _2rdacarrier
490	1		_aChapman & Hall/CRC monographs on statistics & applied probability ; _v129
504			_aIncludes bibliographical references and index.
505	0		_a1. Introduction -- 2. Mathematical foundation -- 3. Constrained Principal Component Analysis (CPCA) -- 4. Special cases and related methods -- 5. Related topics of interest -- 6. Different Constraints on Different Dimensions (DCDD).
520			_a"In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? What kind of benefits are we getting from them? Addressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified framework for these approaches.The book begins with four concrete examples of CPCA that provide readers with a basic understanding of the technique and its applications. It gives a detailed account of two key mathematical ideas in CPCA: projection and singular value decomposition. The author then describes the basic data requirements, models, and analytical tools for CPCA and their immediate extensions. He also introduces techniques that are special cases of or closely related to CPCA and discusses several topics relevant to practical uses of CPCA. The book concludes with a technique that imposes different constraints on different dimensions (DCDD), along with its analytical extensions. MATLAB� programs for CPCA and DCDD as well as data to create the book's examples are available on the author's website"-- _cProvided by publisher.
530			_aAlso available in print format.
650		0	_aPrincipal components analysis.
650		0	_aMultivariate analysis.
655		7	_aElectronic books. _2lcsh
776	0	8	_iPrint version: _z9781466556669 (hardback)
830		0	_aMonographs on statistics and applied probability (Series) ; _v129.
856	4	0	_uhttp://marc.crcnetbase.com/isbn/9781466556683 _qapplication/PDF
999			_c11386 _d11385