Linearized Kernel Dictionary Learning

The work presented in this paper describes a new approach for incorporating kernels into dictionary learning. In order to do so, we first approximate the kernel matrix using a cleverly sampled subset of its columns using the Nystrom method; secondly, as we wish to avoid using this matrix altogether, we decompose it by SVD to form new “virtual samples”, on which any linear dictionary learning can be employed.

Our method, termed “Linearized Kernel Dictionary Learning” (LKDL) can be seamlessly applied as a pre-processing stage on top of any efficient off-the-shelf dictionary learning scheme, effectively “kernelizing” it. In the paper we demonstrate the effectiveness of our method on several tasks of both supervised and unsupervised classification and show the efficiency of the proposed scheme, its easy integration and performance boosting properties. The following freely available package contains the data and Matlab scripts of all the simulations presented in the above mentioned paper.