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Adaptive data-driven dictionaries for sparse approximations provide superior performance compared to predefined dictionaries in applications involving representation and classification of data. In this paper, we propose a novel algorithm for learning global dictionaries particularly suited to the sparse representation of natural images. The proposed algorithm uses a hierarchical energy based learning...
We present an analytic and geometric view of the sample mean of graphs. The theoretical framework yields efficient subgradient methods for approximating a structural mean and a simple plug-in mechanism to extend existing central clustering algorithms to graphs. Experiments in clustering protein structures show the benefits of the proposed theory.
To improve the training speed of SVM, we propose a new SVM training approach which takes thick convex-hull as training set. The approach makes better use of the margin information for classification of data sets, and thus extends the use of convex hull to approximately linearly separable problems. Experiments on 5 UCI data sets indicate that the approach speeds up training of SVM with guarantee of...
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