Kernelized discriminative-collaborative representation-based approach for pattern classification

doi:10.1016/j.compeleceng.2022.108342

Kernelized discriminative-collaborative representation-based approach for pattern classification

2022-10-01

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DOI：10.1016/j.compeleceng.2022.108342

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In representation-based classification methods, Gaussian function is adopted for realizing the nonlinear representation of samples. However, there are some problems. The definition of Gaussian kernel includes exponential operation, which causes the parameters to be sensitive and consumes exponential computational time. In addition, the performance of pattern classification may be dropped with noise (outliers). To solve these problems, a new Euclidean Kernel is designed to replace Gaussian Kernel. Moreover, a new weight and an uncorrelated sparsity constraint are introduced to enhance within-class representation consistency and between -class representation discrepancy. Then, we propose a kernelized discriminative-collaborative representation-based approach for pattern classification by incorporating the cooperative and competitive representation. The proposed model fully excavates the intrinsic factors of the samples and presents promising performance and better interpretability. Finally, extensive experiments are conducted in comparison with other popular methods on different types of databases, and these experiments verify that the designed approach performs better than its competitors.

Keywords:

Pattern classification

Nonlinear representation

Uncorrelated sparsity constraint

Discriminative-collaborative representation

Kernelized discriminative-collaborative representation-based approach for pattern classification

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