| 000 | 03344nam a2200469 i 4500 | ||
|---|---|---|---|
| 001 | EBC1402688 | ||
| 003 | MiAaPQ | ||
| 005 | 20240120144911.0 | ||
| 006 | m o d | | ||
| 007 | cr cnu|||||||| | ||
| 008 | 130930t20142014flud ob 000 0 eng|d | ||
| 020 | _z9781466556669 (hardback) | ||
| 020 | _a9781466556683 (e-book) | ||
| 035 | _a(MiAaPQ)EBC1402688 | ||
| 035 | _a(Au-PeEL)EBL1402688 | ||
| 035 | _a(CaPaEBR)ebr10800725 | ||
| 035 | _a(CaONFJC)MIL543667 | ||
| 035 | _a(OCoLC)862827064 | ||
| 040 |
_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ |
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| 050 | 4 |
_aQA278.5 _b.T35 2014 |
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| 082 | 0 |
_a519.5/35 _223 |
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| 100 | 1 | _aTakane, Yoshio. | |
| 245 | 1 | 0 |
_aConstrained principal component analysis and related techniques / _cYoshio Takane. |
| 264 | 1 |
_aBoca Raton : _bChapman and Hall/CRC, _c[2014] |
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| 264 | 4 | _c2014 | |
| 300 |
_a1 online resource (244 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_acomputer _2rdamedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 |
_aMonographs on statistics and applied probability ; _v129 |
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| 504 | _aIncludes bibliographical references. | ||
| 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. |
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| 588 | _aDescription based on print version record. | ||
| 590 | _aElectronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries. | ||
| 650 | 0 | _aPrincipal components analysis. | |
| 650 | 0 | _aMultivariate analysis. | |
| 655 | 4 | _aElectronic books. | |
| 776 | 0 | 8 |
_iPrint version: _aTakane, Yoshio. _tConstrained principal component analysis and related techniques. _dBoca Raton : Chapman and Hall/CRC, [2014] _hxvii, 224 pages _kMonographs on statistics and applied probability ; 129 _z9781466556669 _w(DLC) 2013039504 |
| 797 | 2 | _aProQuest (Firm) | |
| 830 | 0 |
_aMonographs on statistics and applied probability (Series) ; _v129. |
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| 856 | 4 | 0 |
_uhttps://ebookcentral.proquest.com/lib/bacm-ebooks/detail.action?docID=1402688 _zClick to View |
| 999 |
_c101939 _d101939 |
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