Topology-guided HD computing encodes discrete holes and RTS-invariant descriptors (Zernike for outer shape, Fourier for holes) into hypervectors with learned reliability weights, yielding substantially higher robustness on corrupted MNIST/EMNIST than naive HD baselines while matching compact CNNs on
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Encoding Robust Topological Signatures for Hyperdimensional Computing
Topology-guided HD computing encodes discrete holes and RTS-invariant descriptors (Zernike for outer shape, Fourier for holes) into hypervectors with learned reliability weights, yielding substantially higher robustness on corrupted MNIST/EMNIST than naive HD baselines while matching compact CNNs on