L1-norm locally linear representation regularization multi-source adaptation learning

Neural Networks

Volumn 69, Issue , 2015, Pages 80-98

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

^a ZHEJIANG UNIVERSITY   (China)

^b HUZHOU UNIVERSITY   (China)

Author keywords

Graph regularization; Graph based semi supervised learning; L1 norm locally linear representation; Multi source adaptation learning

Indexed keywords

GEOMETRY; GRAPHIC METHODS; LAPLACE TRANSFORMS; LEARNING ALGORITHMS; LUNAR SURFACE ANALYSIS; MAPPING; PROBABILITY DISTRIBUTIONS;

DOMAIN ADAPTATION LEARNING; EXTRACT INFORMATIONS; GRAPH REGULARIZATION; LINEAR REPRESENTATION; MARGINAL DISTRIBUTION; MULTI-SOURCES; REPRODUCING KERNEL HILBERT SPACES; SEMI- SUPERVISED LEARNING;

SUPERVISED LEARNING;

ADAPTATION LEARNING; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DOMAIN ADAPTATION LEARNING TASK; GEOMETRY; KERNEL METHOD; L1 NORM LOCALLY LINEAR REPRESENTATION METHOD; LEARNING ALGORITHM; LINEAR SYSTEM; MACHINE LEARNING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NETWORK LEARNING; PATTERN RECOGNITION; PRIORITY JOURNAL; PROBABILITY; PROBLEM BASED LEARNING; TASK PERFORMANCE; ALGORITHM; HUMAN; NONLINEAR SYSTEM; STATISTICAL MODEL; SUPERVISED MACHINE LEARNING;

ALGORITHMS; HUMANS; LINEAR MODELS; NONLINEAR DYNAMICS; SUPERVISED MACHINE LEARNING;

EID: 84935918088     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2015.01.009     Document Type: Article

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