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- W3152038104 abstract "Collaboration representation-based classifica- tion (CRC) was proposed as an alternative approach to the sparse representation method with similar efficiency. The CRC is essentially a competition scheme for the training samples to compete with each other in representing the test sample, and the training class with the minimum repre- sentation residual from the test sample wins the competi- tion in the classification. However, the representation error is usually calculated based on the Euclidean distance between a test sample and the weighted sum of all the same-class samples. This paper exploits alternative meth- ods of calculating the representation error in the CRC methods to reduce the representation residual in a more optimal way, so that the sample classes compete with each other in a closer range to represent the test sample. A large number of face recognition experiments on three face image databases show that the CRC methods with opti- mized presentation residual achieve better performance than the original CRC, and the maximum improvement in classification accuracy is up to 12 %." @default.
- W3152038104 created "2021-04-13" @default.
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- W3152038104 date "2014-01-01" @default.
- W3152038104 modified "2023-09-24" @default.
- W3152038104 title "Collaborative representation with reduced residual for face recognition" @default.
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