Kernel Laplacian sparse coding for image classification
Zhang Li-h · Dalian Ligong Daxue xuebao · 2015
Sparse coding can achieve good performance in some computer vision problems.However,past sparse coding was implemented in the original feature space.Kernel method can acquire high dimensional nonlinear mapping characteristics.Inspired by it,the Laplacian sparse coding(LSc)is extended,and the kernel Laplacian sparse coding(KLSc)is proposed.It can reduce the feature quantization error and enhance the sparse coding performance.Experimental results of three standard datasets show that the proposed image classification algorithm based on KLSc has good classification effect,and the correct classification rate is better than that of LSc method.