Incorporating the loss function into discriminative clustering of structured outputs

IEEE Transactions on Neural Networks

Volumn 21, Issue 10, 2010, Pages 1564-1575

  Zhong, Wenliang ^a   Pan, Weike ^a   Kwok, James T ^a   Tsang, Ivor W ^b  

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

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Clustering methods; dependence maximization; Hilbert Schmidt independence criterion; loss; structured clustering

Indexed keywords

CLUSTERING ACCURACY; CLUSTERING METHODS; CLUSTERING PROCESS; DATA SETS; DEPENDENCE MAXIMIZATION; DISCRIMINATIVE CLUSTERING; HILBERT; HILBERT SCHMIDT INDEPENDENCE CRITERION; LOSS FUNCTIONS; MATRIX; OPTIMIZATION PROBLEMS; STRUCTURE INFORMATION; STRUCTURED CLUSTERING; ZERO-ONE;

CLUSTER ANALYSIS; OPTIMIZATION;

CLUSTERING ALGORITHMS;

PROTEIN;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; CLUSTER ANALYSIS; FACIAL EXPRESSION; FACTUAL DATABASE;

ALGORITHMS; CLUSTER ANALYSIS; DATABASES, FACTUAL; FACIAL EXPRESSION; NEURAL NETWORKS (COMPUTER); PROTEINS;

EID: 77957819193     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2010.2064177     Document Type: Article

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