000			03805nam a2200589 i 4500
001			6963191
003			IEEE
005			20190220121651.0
006			m o d
007			cr |n|||||||||
008			151223s2014 maua ob 001 eng d
010			_z 2014003812 (print)
020			_a9780262325325 _qelectronic
020			_z9780262027724 _qhardcover
035			_a(CaBNVSL)mat06963191
035			_a(IDAMS)0b0000648284f7e4
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aTA342.P73 2014eb
082	0	0	_a003/.74 _223
245	0	0	_aPractical applications of sparse modeling / _cedited by Irina Rish, Guillermo A. Cecchi, Aurelie Lozano, and Alexandru Niculescu-Mizil.
264		1	_aCambridge, Massachusetts : _bMIT Press, _c[2014]
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2014]
300			_a1 PDF (xii, 249 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aNeural information processing series
504			_aIncludes bibliographical references and index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aSparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including neuroscience, computational biology, and computer vision. Sparse modeling methods can improve the interpretability of predictive models and aid efficient recovery of high-dimensional unobserved signals from a limited number of measurements. Yet despite significant advances in the field, a number of open issues remain when sparse modeling meets real-life applications. The book discusses a range of practical applications and state-of-the-art approaches for tackling the challenges presented by these applications. Topics considered include the choice of method in genomics applications; analysis of protein mass-spectrometry data; the stability of sparse models in brain imaging applications; sequential testing approaches; algorithmic aspects of sparse recovery; and learning sparse latent models.ContributorsA. Vania Apkarian, Marwan Baliki, Melissa K. Carroll, Guillermo A. Cecchi, Volkan Cevher, Xi Chen, Nathan W. Churchill, R�mi Emonet, Rahul Garg, Zoubin Ghahramani, Lars Kai Hansen, Matthias Hein, Katherine Heller, Sina Jafarpour, Seyoung Kim, Mladen Kolar, Anastasios Kyrillidis, Aurelie Lozano, Matthew L. Malloy, Pablo Meyer, Shakir Mohamed, Alexandru Niculescu-Mizil, Robert D. Nowak, Jean-Marc Odobez, Peter M. Rasmussen, Irina Rish, Saharon Rosset, Martin Slawski, Stephen C. Strother, Jagannadan Varadarajan, Eric P. Xing.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aTitle from PDF.
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aSparse matrices.
650		0	_aData reduction.
650		0	_aSampling (Statistics)
650		0	_aMathematical models.
655		0	_aElectronic books.
695			_aEpitaxial layers
695			_aExcitons
695			_aNitrogen
695			_aRadiative recombination
695			_aSilicon carbide
695			_aTemperature measurement
700	1		_aRish, Irina, _d1969-, _eeditor.
710	2		_aIEEE Xplore (Online Service), _edistributor.
710	2		_aMIT Press, _epublisher.
776	0	8	_iPrint version _z9780262027724
830		0	_aNeural information processing series.
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6963191
999			_c39654 _d39654