Sparse coding has been widely and successfully used in image classification, noise reduction, texture synthesis, and audio processing. Although existing sparse coding methods can produce promising results, they failed to consider the high dimensional manifold information within data. In this paper, we propose a dual graph regularized sparse coding method to effectively preserve duality between data points and features for sparse representation. This is achieved by Feature-sign Search with Lagrange Dual (FS-LD) algorithm and Least-Angle Regression with Block Coordinate Descent (LARS-BCD) algorithm. Experimental results in clustering and classification show that the proposed method outperforms other existing methods.