Manifold preserving: An intrinsic approach for semisupervised distance metric learning

IEEE Transactions on Neural Networks and Learning Systems

Volumn 29, Issue 7, 2018, Pages 2731-2742

  Ying, Shihui ^a   Wen, Zhijie ^a   Shi, Jun ^a   Peng, Yaxin ^a,b   Peng, Jigen ^c   Qiao, Hong ^d  

^a SHANGHAI UNIVERSITY   (China)

^b EAST CHINA NORMAL UNIVERSITY   (China)

^c XI'AN JIAOTONG UNIVERSITY   (China)

^d INSTITUTE OF AUTOMATION   (China)

Author keywords

Classification; distance metric learning; intrinsic algorithm; matrix manifold; semisupervised learning

Indexed keywords

COMPUTATIONAL EFFICIENCY; MATRIX ALGEBRA; NUMERICAL METHODS; STEEPEST DESCENT METHOD;

ADAPTIVE PARAMETERS; DISTANCE METRIC LEARNING; MANIFOLD STRUCTURES; METRIC INFORMATION; MINIMIZATION PROBLEMS; NUMERICAL RESULTS; SYMMETRIC POSITIVE DEFINITE; SYMMETRIC POSITIVE DEFINITE MATRICES;

ITERATIVE METHODS;

EID: 85019867612     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2017.2691005     Document Type: Article

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