A Supervised Low-Rank Method for Learning Invariant Subspaces

Sparse representation and low-rank matrix decomposition approaches have been successfully applied to several computer vision problems. They build a generative representation of the data, which often requires complex training as well as testing to be robust against data variations induced by nuisance factors... (read more)

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