Multi-source adaptation joint kernel sparse representation for visual classification

Neural Networks

Volumn 76, Issue , 2016, Pages 135-151

  Tao, JianWen ^a   Hu, Wenjun ^b   Wen, Shiting ^a  

^a ZHEJIANG UNIVERSITY   (China)

^b HUZHOU UNIVERSITY   (China)

Author keywords

Graph based semi supervised learning; Joint kernel sparse representation; Maximum mean discrepancy; Multi source domain adaptation

Indexed keywords

ALGORITHMS; GRAPHIC METHODS; OPTIMIZATION;

COMPUTATIONAL MODEL; DOMAIN ADAPTATION; KERNEL HILBERT SPACE; MAXIMUM MEAN DISCREPANCY; OPTIMIZATION PROBLEMS; SEMI- SUPERVISED LEARNING; SPARSE REPRESENTATION; VISUAL CLASSIFICATION;

SUPERVISED LEARNING;

ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSICAL GRAPH BASED SEMI SUPERVISED LEARNING; CLASSIFICATION ALGORITHM; CONCEPTUAL FRAMEWORK; DOMAIN ADAPTATION LEARNING METHOD; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL ANALYSIS; MULTI SOURCE ADAPTATION REGULARIZATION JOINT KERNEL SPARSE REPRESENTATION FRAMEWORK; PRIORITY JOURNAL; PROBABILITY; PROCESS OPTIMIZATION; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINE; VISUAL SYSTEM; ALGORITHM; COMPUTER SIMULATION; THEORETICAL MODEL;

ALGORITHMS; COMPUTER SIMULATION; MACHINE LEARNING; MODELS, THEORETICAL;

EID: 84958167803     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2016.01.008     Document Type: Article

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