Learning subspace kernels for classification

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2008, Pages 106-114

  Chen, Jianhui ^a,b   Ji, Shuiwang ^a,b   Ceran, Betul ^a,b   Li, Qi ^c   Wu, Mingrui ^d   Ye, Jieping ^a,b  

^a Arizona State University   (United States)

^b ARIZONA STATE UNIVERSITY   (United States)

^c WESTERN KENTUCKY UNIVERSITY   (United States)

^d YAHOO INC   (United States)

Author keywords

Classification; Hilbert Schmidt independence criterion; Subspace kernel; Support vector machines

Indexed keywords

BENCHMARK DATUM; CLASSIFICATION; COLUMN GENERATIONS; DATA MINING TASKS; EIGENVALUE PROBLEMS; FEATURE SPACES; HILBERT-SCHMIDT INDEPENDENCE CRITERION; JOINT OPTIMIZATIONS; KERNEL LEARNING; KERNEL METHODS; LEARNING FORMULATIONS; LEARNING SUBSPACES; LINEAR PROGRAMS; LOW-DIMENSIONAL SUBSPACES; MULTIPLE KERNEL LEARNING; MULTIPLE KERNELS; OPTIMAL SUBSPACES; REDUNDANT INFORMATIONS; SEMI INFINITES; SEMIDEFINITE PROGRAMS; SUBSPACE KERNEL; UNCORRELATED SUBSPACES;

EIGENVALUES AND EIGENFUNCTIONS; FACE RECOGNITION; IMAGE RETRIEVAL; KNOWLEDGE MANAGEMENT; MINING; SUPPORT VECTOR MACHINES;

EDUCATION;

EID: 65449122452     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1401890.1401908     Document Type: Conference Paper

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