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Discriminant Projection Representation-based Classification for Vision Recognition
Representation-based classification methods such as sparse
representation-based classification (SRC) and linear regression classification
(LRC) have attracted a lot of attentions. In order to obtain the better
representation, a novel method called projection representation-based
classification (PRC) is proposed for image recognition in this paper. PRC is
based on a new mathematical model. This model denotes that the 'ideal
projection' of a sample point on the hyper-space may be gained by
iteratively computing the projection of on a line of hyper-space with
the proper strategy. Therefore, PRC is able to iteratively approximate the
'ideal representation' of each subject for classification. Moreover, the
discriminant PRC (DPRC) is further proposed, which obtains the discriminant
information by maximizing the ratio of the between-class reconstruction error
over the within-class reconstruction error. Experimental results on five
typical databases show that the proposed PRC and DPRC are effective and
outperform other state-of-the-art methods on several vision recognition tasks.Comment: Accepted by the Thirty-Second AAAI Conference on Artificial
Intelligence (AAAI-18
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