Maximum margin clustering made practical

IEEE Transactions on Neural Networks

Volumn 20, Issue 4, 2009, Pages 583-596

  Zhang, Kai ^a,b   Tsang, Ivor W ^a,c   Kwok, James T ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^b LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Large margin methods; Maximum margin clustering (MMC); Scalability; Unsupervised learning

Indexed keywords

DATA SETS; ITERATIVE PROCEDURES; LARGE MARGIN METHODS; LOSS FUNCTIONS; MAXIMUM MARGIN CLUSTERING (MMC); NON-CONVEX PROBLEMS; NONCONVEX; NONCONVEX OPTIMIZATION PROBLEMS; OPTIMIZATION PROBLEMS; PREMATURE CONVERGENCES; REAL-WORLD DATUM; SDP RELAXATIONS; SEMIDEFINITE PROGRAMS; SMALL DATA SETS;

EDUCATION; OPTIMIZATION; SCALABILITY; UNSUPERVISED LEARNING;

METALLIC MATRIX COMPOSITES;

EID: 67349089521     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2008.2010620     Document Type: Article

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