Abstract: Recently, dictionary learning (DL) has been applied to various pattern recognition tasks successfully, analysis dictionary learning, however as an important branch of dictionary learning, has not been fully exploited due to its poor discriminability. In this paper, a novel robust and discriminative analysis dictionary learning method is proposed, which specially seeks low rank representation from noisy data and learn a discriminative dictionary from the recovered clean data by incorporating with the Fisher criterion. The discriminability of dictionary is improved by introducing the supervised mechanism. At last, the task of human action recognition is conducted by applying the proposed method. Experiments on several human action recognition datasets show that the proposed method outperforms other classical synthesis dictionary methods.