Computers & Electrical Engineering | Vol.84, Issue. | 2020-05-31 | Pages 106632
Double competitive constraints-based collaborative representation for pattern classification
Representation-based classification (RBC) has attracted much attention in pattern recognition. As a linear representative RBC method, collaborative representation-based classification (CRC) is very promising for classification. Although many extensions of CRC have been developed recently, the discriminative and competitive representations of different classes for favorable classification has not yet be fully explored. To design the discriminative and competitive collaborative representations for enhancing the power of pattern discrimination, we propose a novel double competitive constraints-based collaborative representation for classification (DCCRC). In the proposed DCCRC, one competitive constraint is the l2-norm regularization of residuals between each query sample and the class-specific representations, another one is the l2-norm regularization of the representations of all the classes excluding any one class. In two competitive constraints, the class discrimination information is employed to generate competitive representations. Moreover, the proposed method integrates both the representation learning and classification into the unified model. The effective and robust classification performance of the proposed method is verified by conducting extensive experiments on six public face databases and twelve real numerical UCI data sets in comparisons with the state-of-the-art CRC methods. The experimental results demonstrate the promising classification performance of the proposed method.
Original Text (This is the original text for your reference.)
Double competitive constraints-based collaborative representation for pattern classification
Representation-based classification (RBC) has attracted much attention in pattern recognition. As a linear representative RBC method, collaborative representation-based classification (CRC) is very promising for classification. Although many extensions of CRC have been developed recently, the discriminative and competitive representations of different classes for favorable classification has not yet be fully explored. To design the discriminative and competitive collaborative representations for enhancing the power of pattern discrimination, we propose a novel double competitive constraints-based collaborative representation for classification (DCCRC). In the proposed DCCRC, one competitive constraint is the l2-norm regularization of residuals between each query sample and the class-specific representations, another one is the l2-norm regularization of the representations of all the classes excluding any one class. In two competitive constraints, the class discrimination information is employed to generate competitive representations. Moreover, the proposed method integrates both the representation learning and classification into the unified model. The effective and robust classification performance of the proposed method is verified by conducting extensive experiments on six public face databases and twelve real numerical UCI data sets in comparisons with the state-of-the-art CRC methods. The experimental results demonstrate the promising classification performance of the proposed method.
+More
constraint linear representative rbc method collaborative representationbased classification crc double competitive constraintsbased collaborative representation for classification dccrc favorable classification stateoftheart crc discriminative and competitive collaborative representations real numerical uci data sets classspecific public face databases constraints representation learning l2norm regularization of residuals unified model method class discrimination information
Select your report category*
Reason*
New sign-in location:
Last sign-in location:
Last sign-in date: