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001 978-3-319-78674-2
003 DE-He213
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008 180416s2018 sz | s |||| 0|eng d
020 _a9783319786742
_9978-3-319-78674-2
024 7 _a10.1007/978-3-319-78674-2
_2doi
050 4 _aTK5102.9
072 7 _aTJF
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aTJF
_2thema
072 7 _aUYS
_2thema
082 0 4 _a621.382
_223
100 1 _aDumitrescu, Bogdan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_945779
245 1 0 _aDictionary Learning Algorithms and Applications
_h[electronic resource] /
_cby Bogdan Dumitrescu, Paul Irofti.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIV, 284 p. 48 illus., 47 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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 _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.
650 0 _aSignal processing.
_94052
650 0 _aEngineering mathematics.
_93254
650 0 _aEngineering—Data processing.
_931556
650 0 _aElectronic circuits.
_919581
650 0 _aComputer networks .
_931572
650 1 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aMathematical and Computational Engineering Applications.
_931559
650 2 4 _aElectronic Circuits and Systems.
_945780
650 2 4 _aComputer Communication Networks.
_945781
700 1 _aIrofti, Paul.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_945782
710 2 _aSpringerLink (Online service)
_945783
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319786735
776 0 8 _iPrinted edition:
_z9783319786759
776 0 8 _iPrinted edition:
_z9783030087616
856 4 0 _uhttps://doi.org/10.1007/978-3-319-78674-2
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c77739
_d77739