A generative semi-supervised model for multi-view learning when some views are label-free

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2013, Pages 3302-3306

  Jin, Gaole ^a   Raich, Raviv ^a   Miller, David J ^b  

^a Oregon State University   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Expectation Maximization; mixture of factors analyzers; multi view learning; semi supervised learning

Indexed keywords

COMPUTATIONALLY EFFICIENT; DIGIT RECOGNITION; DISCRIMINATIVE APPROACH; EXPECTATION MAXIMIZATION; MULTI-VIEW LEARNING; MULTI-VIEW MODELING; REDUCED COMPLEXITY; SEMI-SUPERVISED LEARNING;

ALGORITHMS; SIGNAL PROCESSING; SUPERVISED LEARNING;

MIXTURES;

EID: 84890492779     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6638269     Document Type: Conference Paper

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