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Multiresolution analysis is important for understanding graph signals, which represent graph-structured data. Wavelet filterbanks permit multiscale analysis and processing of graph signals—particularly, useful for harvesting large-scale data. Inspired by first-order spline wavelets in classical signal processing, we introduce two-channel (low-pass and high-pass) wavelet filterbanks for graph signals...
Graph semi-supervised learning (GSSL) is a technique that uses a combination of labeled and unlabeled nodes on a graph to determine a classifier for new, incoming data. This problem can be analyzed through the lens of graph signal processing. In particular, the penalty functions used in the optimization formulation of standard GSSL algorithms can be interpreted as appropriately-defined filters in...
Inspired by first-order spline wavelets in classical signal processing, we introduce two-channel (low-pass and high-pass), critically-sampled, perfect-reconstruction filterbanks for signals defined on circulant graphs, which accommodate linear shift-invariant filtering. We then generalize to filters that process signals defined on noncirculant graphs. We apply these filters, which can be tuned to...
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