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In this paper, we present a spectral clustering method for online and streaming applications. Here, we note that the rank of the coefficients of the eigenvector of the graph Laplacian govern, together with the weights of the adjacency matrix, the assignment of the data to clusters. Thus, we adopt a sampling without replacement strategy, where, at each sampling step, we select those data instances...
Here, we turn our attention to barycentric embeddings and examine their utility for semi-supervised image labelling tasks. To this end, we view the pixels in the image as vertices in a graph and their pairwise affinities as weights of the edges between them. Abstracted in this manner, we can pose the semi-supervised labelling problem into a graph theoretic setting where the labels are assigned based...
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...
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