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Generalized Multiview Analysis: A discriminative latent space
2012
2012 IEEE Conference on Computer Vision and Pattern Recognition
This paper presents a general multi-view feature extraction approach that we call Generalized Multiview Analysis or GMA. GMA has all the desirable properties required for cross-view classification and retrieval: it is supervised, it allows generalization to unseen classes, it is multi-view and kernelizable, it affords an efficient eigenvalue based solution and is applicable to any domain. GMA exploits the fact that most popular supervised and unsupervised feature extraction techniques are the
doi:10.1109/cvpr.2012.6247923
dblp:conf/cvpr/SharmaKDJ12
fatcat:aeqgsz62hfesbjwq4pikwr6tj4