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Latent Dictionary Learning for Sparse Representation Based Classification
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
Dictionary learning (DL) for sparse coding has shown promising results in classification tasks, while how to adaptively build the relationship between dictionary atoms and class labels is still an important open question. The existing dictionary learning approaches simply fix a dictionary atom to be either class-specific or shared by all classes beforehand, ignoring that the relationship needs to be updated during DL. To address this issue, in this paper we propose a novel latent dictionary
doi:10.1109/cvpr.2014.527
dblp:conf/cvpr/YangDSG14
fatcat:lsonmzozzzcbbde72iyltlrlla