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• W2294941227 abstract "In this paper, we propose the two-phase representation based classification called the two-phase linear reconstruction measure based classification (TPLRMC). It is inspired from the fact that the linear reconstruction measure (LRM) gauges the similarities among feature samples by decomposing each feature sample as a liner combination of the other feature samples with (L_{p})-norm regularization. Since the linear reconstruction coefficients can fully reveal the feature’s neighborhood structure that is hidden in the data, the similarity measures among the training samples and the query sample are well provided in classifier design. In TPLRMC, it first coarsely seeks the K nearest neighbors for the query sample with LRM, and then finely represents the query sample as the linear combination of the determined K nearest neighbors and uses LRM to perform classification. The experimental results on face databases show that TPLRMC can significantly improve the classification performance." @default.
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• W2294941227 date "2015-01-01" @default.
• W2294941227 modified "2023-09-27" @default.
• W2294941227 title "Two-Phase Representation Based Classification" @default.
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• W2294941227 doi "https://doi.org/10.1007/978-3-319-24075-6_26" @default.
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