000 02508cam a2200457 i 4500
001 22286620
003 OSt
005 20240729113437.0
008 211026t20192019enk b 001 0 eng d
010 _a 2021277362
020 _a9780128136591
_qpaperback
020 _a0128136596
_qpaperback
020 _z9780128136607
_qelectronic book
035 _a(OCoLC)on1022780543
040 _aYDX
_beng
_cTUPM
_erda
_dBDX
_dOCLCQ
_dWAU
_dYDXIT
_dOCLCF
_dDLC
042 _alccopycat
050 0 _aGS Q
_b325.5 D44 2019
245 0 0 _aDeep learning through sparse and low-rank modeling /
_cedited by Zhangyang Wang, Yun Fu, Thomas S. Huang.
264 1 _aLondon ;
_aSan Diego :
_bAcademic Press, an imprint of Elsevier,
_c[2019]
264 1 _c©2019
300 _axvii, 277 pages ;
_c24 cm
336 _atext
_btxt
_2rdacontent
337 _aunmediated
_bn
_2rdamedia
338 _avolume
_bnc
_2rdacarrier
490 0 _aComputer vision and pattern recognition series
504 _aIncludes bibliographical references and index.
520 _aDeep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.
650 1 0 _aMachine learning.
650 1 0 _aBig data.
650 1 0 _aData mining.
650 1 0 _aBig data.
_2fast
_0(OCoLC)fst01892965
650 1 0 _aData mining.
_2fast
_0(OCoLC)fst00887946
650 1 0 _aMachine learning.
_2fast
_0(OCoLC)fst01004795
700 1 _aWang, Zhangyang,
_eeditor.
700 1 _aFu, Yun,
_eeditor.
700 1 _aHuang, Thomas S.,
_d1936-
_eeditor.
830 0 _aComputer vision and pattern recognition series.
906 _a7
_bcbc
_ccopycat
_d2
_encip
_f20
_gy-gencatlg
942 _2lcc
_cBK
999 _c3330
_d3330