Tensor Least Angle Regression for Sparse Representations of Multidimensional Signals.

Sparse signal representations have gained much interest recently in both signal processing and statistical communities. Compared to orthogonal matching pursuit (OMP) and basis pursuit, which solve the and constrained sparse least-squares problems, respectively, least angle regression (LARS) is a computationally efficient method to solve both problems for all critical values of the regularization parameter . However, all of these methods are not suitable for solving large multidimensional sparse least-squares problems, as they would require extensive computational power and memory.