6. The method of claim 5 wherein said optimization problem is given by minimize =  J ⁡ ( H ∘ S )  row , 0 subject ⁢ ⁢ to ⁢ ⁢  Y - AS  F < ɛ where H = [ 1 0 … 0 0 1 … 0 ⋮ ⋮ ⋮ ⋮ 0 0 … 1 ] , ⁢ J = [ I KT , I KT , … ⁢ , I KT ] , where operator ∘ indicates an entry-wise matrix Hadamard product, the vectors 1 and 0 in H are in R KT , and contain all ones and zeros respectively, the elements I KT in J are the KT-dimensional identity matrix, ∥ ∥ row,0 represents a row norm indicating the number of non-zero rows in said matrix, and ∥ ∥ F indicates said Frobenius norm.
6. The method of claim 5 wherein said optimization problem is given by minimize =  J ⁡ ( H ∘ S )  row , 0 subject ⁢ ⁢ to ⁢ ⁢  Y - AS  F < ɛ where H = [ 1 0 … 0 0 1 … 0 ⋮ ⋮ ⋮ ⋮ 0 0 … 1 ] , ⁢ J = [ I KT , I KT , … ⁢ , I KT ] , where operator ∘ indicates an entry-wise matrix Hadamard product, the vectors 1 and 0 in H are in R KT , and contain all ones and zeros respectively, the elements I KT in J are the KT-dimensional identity matrix, ∥ ∥ row,0 represents a row norm indicating the number of non-zero rows in said matrix, and ∥ ∥ F indicates said Frobenius norm. 7. The method of claim 6 wherein said optimization problem is solved by formulating an auxiliary convex optimization problem which minimizes Frobenius norm ∥Y−AS∥ F subject to a sparsity constraint defined as a linear membership constraint utilizing a simultaneous orthogonal matching pursuit algorithm.