Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification

IJCAI International Joint Conference on Artificial Intelligence

Volumn 2016-January, Issue , 2016, Pages 1881-1887

  Nie, Feiping ^a   Li, Jing ^a   Li, Xuelong ^b  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; GRAPHIC METHODS; LEARNING ALGORITHMS; VIRTUAL REALITY;

DATA DISTRIBUTION; HETEROGENEOUS FEATURES; MULTI-VIEW CLUSTERING; SEMI- SUPERVISED LEARNING; SEMI-SUPERVISED; SEMI-SUPERVISED CLASSIFICATION; SPECTRAL LEARNING; STATE-OF-THE-ART APPROACH;

SUPERVISED LEARNING;

EID: 85006165458     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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