K-SVD dictionary learning using a fast OMP with applications

K-SVD method has recently been introduced to learn a specific dictionary matrix that best fits a set of training data vectors. K-SVD is flexible in that any preferred pursuit method of sparse coding can be used to represent the data. In this paper, we show how K-SVD method can be used in conjunction with a fast orthogonal matching pursuit implemented using orthogonal projection updating. Geometric interpretation of this learning is also presented. The method was then applied to underwater target detection problem using a dual-channel sonar imagery data.