000			03776nam a2200409 i 4500
710	2		_aSpringerLink (Online service) _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/274647764/ _9106996
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008			180416s2018 gw | s |||| 0|eng d
020			_a9783319786742
024	7		_a10.1007/978-3-319-78674-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA9.58 _b2018 EB
100	1		_aDumitrescu, Bogdan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/no2007041110 _1http://viaf.org/viaf/32204216/ _9673328
245	1	0	_aDictionary Learning Algorithms and Applications _cby Bogdan Dumitrescu, Paul Irofti
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XIV, 284 páginas) _b 48 ilustraciones, 47 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
505	0		_aChapter1: Sparse representations -- Chapter2: Dictionary learning problem -- Chapter3: Standard algorithms -- Chapter4: Regularization and incoherence -- Chapter5: Other views on the DL problem -- Chapter6: Optimizing dictionary size -- Chapter7: Structured dictionaries -- Chapter8: Classification -- Chapter9: Kernel dictionary learning -- Chapter10: Cosparse representations.
520	3		_aThis book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, including shift invariant; orthogonal blocks or factored dictionaries; and separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book is accompanied by code for all algorithms and for reproducing most tables and figures. Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation; Covers all dictionary structures that are meaningful in applications; Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm.
988			_aEBSPRINGER_2018
650		7	_2embne _aAlgoritmos _9141162
650		7	_2embne _aIngeniería de sistemas _9138451
700	1		_aIrofti, Paul _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673329
776	0	8	_iEdición impresa: _z9783319786735
776	0	8	_iEdición impresa: _z9783319786759
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-78674-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2020 _dz _ek _zSI