New Sheaf Signal Processing Framework for Heterogeneous Network Data

This paper introduces a novel framework for processing heterogeneous network signals, termed Sheaf Signal Processing (SSP), which utilizes network sheaves to model diverse local data structures and their interrelations. Unlike existing graph and topological signal processing methods, SSP jointly models these heterogeneous spaces and the transformations between them. The framework includes a Sheaf Fourier Transform (SFT) to analyze signal inconsistencies related to network topology and local geometry, alongside polynomial sheaf filters and a method for sampling signals by selecting network nodes and intra-node components. Experiments on synthetic, motion-capture, and financial data show improvements over traditional graph signal processing techniques. AI

RANK_REASON The item is a research paper detailing a new signal processing framework. [lever_c_demoted from research: ic=1 ai=0.7]

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• Graph Signal Processing
• Network sheaves
• Representation sheaves
• Sheaf signal processing
• Topological Signal Processing

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