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We propose a sampling theory for finite-dimensional vectors with a generalized bandwidth restriction, which follows the same paradigm of the classical sampling theory. We use this general result to derive a sampling theorem for bandlimited graph signals in the framework of discrete signal processing on graphs. By imposing a specific structure on the graph, graph signals reduce to finite discrete-time...
We present a multiresolution classification framework with semi-supervised learning on graphs with application to the indirect bridge structural health monitoring. Classification in real-world applications faces two main challenges: reliable features can be hard to extract and few labeled signals are available for training. We propose a novel classification framework to address these problems: we...
We present an adaptive graph filtering approach to semi-supervised classification. Adaptive graph filters combine decisions from multiple graph filters using a weighting function that is optimized in a semi-supervised manner. We also demonstrate the multiresolution property of adaptive graph filters by connecting them to the diffusion wavelets. In our experiments, we apply the adaptive graph filters...
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