An SVD-free Approach to Nonlinear Dictionary Learning based on RVFL

This paper presents a novel nonlinear dictionary learning algorithm leveraging the theory of a feed-forward neural network called Random Vector Functional Link (RVFL). The proposed RVFL-based nonlinear Dictionary Learning (RVFLDL) learns a dictionary as a sparse-to-dense feature map from nonlinear sparse coefficients to the dense input features. Kernel-based nonlinear dictionary learning methods operate in a feature space obtained by an implicit feature map, and they are not independent of computationally expensive operations like Singular Value Decomposition (SVD). Training the RVFL-based …

algorithm cs.cv dictionary feature features free functional kernel map network neural network novel paper random svd theory vector