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In this paper, we employ graph embeddings for classification tasks. To do this, we explore the relationship between kernel matrices, spaces of inner products and statistical inference by viewing the embedding vectors for the nodes in the graph as a field on a Riemannian manifold. This leads to a setting where the inference process may be cast as a Maximum a Posteriori (MAP) estimation over a Gibbs...
Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the material end-members. As an important constraint for NMF, sparsity has been modeled making use of the regularizer. Unfortunately, the ...
In this paper, we tackle the problem of image in painting which aims at removing objects from an image or repairing damaged pictures by replacing the missing regions using the information in the rest of the scene. The image in painting method proposed here builds on an exemplar-based perspective so as to improve the local consistency of the in painted region. This is done by selecting the optimal...
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