Proximal alternating algorithms in dictionary learning
نویسندگان
چکیده
In recent years, sparse coding has been widely used in many applications ranging from image processing to pattern recognition. Most existing sparse coding based applications require solving a class of challenging non-smooth and non-convex optimization problems. In this talk, I will review some proximal alternating algorithms for solving such problem and give rigorous convergence analysis. Experiments show that the proposed method achieves similar results with less computation when compared to widely used methods such as K-SVD.
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