摘要
In recent years,there has been a growing usage of sparse representations in signal processing.This paper revisits theK-SVD,an algorithm for designing overcomplete dictionaries for sparse and redundant representations.We present a newapproach to solve dictionary learning models by combining the alternating direction method of multipliers and the orthogonal matching pursuit.The experimental results show that our approach can reliably obtain better learned dictionary elements and outperform other algorithms.
