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020 _a9783319018812
_9978-3-319-01881-2
024 7 _a10.1007/978-3-319-01881-2
_2doi
035 _ato000542166
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aTK5102.9
050 4 _aTA1637-1638
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082 0 4 _a621.382
_223
100 1 _aBahmani, Sohail.
_eauthor.
_9447930
245 1 0 _aAlgorithms for Sparsity-Constrained Optimization
_helectronic resource
_cby Sohail Bahmani.
260 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXXI, 107 p. 13 illus., 12 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053 ;
_v261
505 0 _aIntroduction -- Preliminaries -- Sparsity-Constrained Optimization -- Background -- 1-bit Compressed Sensing -- Estimation Under Model-Based Sparsity -- Projected Gradient Descent for `p-constrained Least Squares -- Conclusion and Future Work.
520 _aThis thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a"greedy" algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use of previous models.
650 0 _aengineering.
_9224332
650 0 _aComputer vision.
_9274100
650 1 4 _aEngineering.
_9224332
650 2 4 _aSignal, Image and Speech Processing.
_9274103
650 2 4 _aMathematical Applications in Computer Science.
_9412135
650 2 4 _aImage Processing and Computer Vision.
_9303601
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_9567110
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-01881-2
912 _aZDB-2-ENG
999 _c399877