An approach for sequential dictionary learning in nonuniform noise

Abstract

Dictionary learning algorithms have received widespread acceptance when it comes to data analysis and signal representation problems. However, most existing algorithms assume isotropic noise. This is a restrictive assumption as the noise across samples may be nonuniform in a number of real world application. The aim of this article is to propose a sequential dictionary learning algorithm for measurement data vectors affected by nonuniform noise. As most of the existing algorithms, the proposed method is a two stages iterative algorithm where the effect of the noise nature appears in the sparse coding and dictionary update stages. The cost function used to derive the dictionary update stage i..