Kernel dictionary learning
In this paper, we present dictionary learning methods for sparse and redundant signal representations in high dimensional feature space. Using the kernel method, we describe how the well-known dictionary learning approaches such as the method of optimal directions and K-SVD can be made nonlinear. We analyze these constructions and demonstrate their improved performance through several experiments on classification problems. It is shown that nonlinear dictionary learning approaches can provide better discrimination compared to their linear counterparts and kernel PCA, especially when the data is corrupted by noise.