Introduction - If you have any usage issues, please Google them yourself
Dictionary study. For example, a vector is k dimension. I now have a dictionary of k*n, where n>>k, the so-called dictionary learning, is to find a linear expression in the dictionary containing N vectors, which can show the current vector of K dimension. It is called "sparse representation" because the general n is larger than k, such as n=512, k=64. At this time your dictionary must be a Redundant (redundant, excess). So there must be a lot of coefficients in your representation to be 0, so it's called a sparse representation.
Signal sparse representation is to overcomplete dictionary given in as little as possible to represent atomic signal, signal can be more succinct representation, which makes it easier for us to get there in the signal information, more convenient for further signal processing, such as compression, encoding etc.. The focus of r