ABSTRACT
This chapter presents a few combined models involving both sparse and low-rank recovery. These models do not always enjoy the theoretical constructs of sparse reconstruction and low-rank reconstruction but are practical powerful tools that yield very good results. The chapter discusses compressive principal component pursuit and illustrates the derivation of principal component pursuit. It also describes the sparse and low-rank model for solving an inverse problem.
