000			04711nam a2200637 i 4500
001			6813050
003			IEEE
005			20200413152851.0
006			m||||eo||d||||||||
007			cr an |||m|||a
008			081101s2008 caua fob 000 0 eng d
020			_a9781598296600 (electronic bk.)
020			_a9781598296594 (pbk.)
024	7		_a10.2200/S00130ED1V01Y200806AIM004 _2doi
035			_a(OCoLC)235585411
035			_a(CaBNVSL)gtp00531511
040			_aCaBNVSL _cCaBNVSL _dCaBNVSL
050		4	_aQ387 _b.M247 2008
082	0	4	_a006.3/32 _222
100	1		_aMahadevan, Sridhar, _d1960-
245	1	0	_aRepresentation discovery using harmonic analysis _h[electronic resource] / _cSridhar Mahadevan.
250			_a1st ed.
260			_aSan Rafael, Calif (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool Publishers, _c2008.
300			_a1 electronic text (xii, 147 p. : ill.) : _bdigital file.
490	1		_aSynthesis lectures on artificial intelligence and machine learning, _x1939-4616 ; _v#4
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat Reader.
500			_aPart of: Synthesis digital library of engineering and computer science.
500			_aSeries from website.
504			_aIncludes bibliographical references (p. 137-145).
505	0		_aOverview -- Vector spaces -- Fourier bases on graphs -- Multiscale bases on graphs -- Scaling to large spaces -- Case study: State-space planning -- Case study: computer graphics -- Case study: natural language -- Future directions.
506	1		_aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510	0		_aCompendex
510	0		_aINSPEC
510	0		_aGoogle scholar
510	0		_aGoogle book search
520			_aRepresentations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particular Fourier and wavelet analysis. Biometric compression methods, the compact disc, the computerized axial tomography (CAT) scanner in medicine, JPEG compression, and spectral analysis of time-series data are among the many applications of classical Fourier and wavelet analysis. A central goal of this book is to show that these analytical tools can be generalized from their usual setting in (infinite-dimensional) Euclidean spaces to discrete (finite-dimensional) spaces typically studied in many subfields of AI. Generalizing harmonic analysis to discrete spaces poses many challenges: a discrete representation of the space must be adaptively acquired; basis functions are not pre-defined, but rather must be constructed. Algorithms for efficiently computing and representing bases require dealing with the curse of dimensionality. However, the benefits can outweigh the costs, since the extracted basis functions outperform parametric bases as they often reflect the irregular shape of a particular state space. Case studies from computer graphics, information retrieval, machine learning, and state space planning are used to illustrate the benefits of the proposed framework, and the challenges that remain to be addressed. Representation discovery is an actively developing field, and the author hopes this book will encourage other researchers to explore this exciting area of research.
530			_aAlso available in print.
588			_aTitle from PDF t.p. (viewed on Nov. 1, 2008).
650		0	_aKnowledge representation (Information theory) _xMathematics.
650		0	_aWavelets (Mathematics)
650		0	_aFourier analysis.
690			_aArtificial intelligence.
690			_aDimensionality reduction.
690			_aFeature construction.
690			_aHarmonic analysis.
690			_aImage processing.
690			_aInformation retrieval.
690			_aLinear algebra.
690			_aMachine learning.
690			_aNatural language processing.
690			_aState space planning.
730	0		_aSynthesis digital library of engineering and computer science.
830		0	_aSynthesis lectures on artificial intelligence and machine learning ; _v#4.
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6813050
999			_c561634 _d561634