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About this paper
Gabriele D'Acunto, Leonardo Di Nino, Paolo Di Lorenzo, Sergio Barbarossa
The authors present a sheaf signal-processing framework for heterogeneous network data, using local spaces, comparison maps, and compact representations to analyze signals and select samples. In a synthetic portfolio test, a 198-coordinate signal was reconstructed with NMSE on the order of 10^-29 from nine entries, versus 182 for a model-agnostic sampler; this result applies to a nine-dimensional band and rank-sufficient samples.
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