Kernel-based Nonlinear Observation Method For Sparse Coding

In this paper, efficient algorithms are proposed to perform sparse coding for the features lifted to a high-dimensional space via nonlinear mapping. we developed how the well-known sparse coding algorithm Homotopy Iterative Thresholding(HIHT) algorithm can be made nonlinear with the kernel method. We also put forward the corresponding dictionary learning strategy using the Lagrange dual method. The experimental results we tested prove that the application of the kernel sparse coding in the classification problem is significantly improved compared with their linear counterparts.