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The forward greedy selection algorithm of Frank and Wolfe has recently been applied with success to coordinate-wise sparse learning problems, characterized by a tradeoff between sparsity and accuracy. In this paper, we generalize this method to the setup of pursuing sparse representations over a prefixed dictionary. Our proposed algorithm iteratively selects an atom from the dictionary and minimizes the objective function over the linear combinations of all the selected atoms. The rate of convergence of this greedy selection procedure is analyzed. Furthermore, we extend the algorithm to the setup of learning nonnegative and convex sparse representation over a dictionary. Applications of the proposed algorithms to sparse precision matrix estimation and low-rank subspace segmentation are investigated with efficiency and effectiveness validated on benchmark datasets.