Real-Valued Hyperdimensional Sequence Representations with Hadamard Product Binding and Shift Equivariance
Encoding temporal order is a fundamental requirement for sequence representations in Hyperdimensional Computing. Fractional Power Encoding provides similarity-preserving position vectors whose inner products approximate shift-invariant kernels, and it supports shift-equivariant transformations of encoded sequence representations. However, standard formulations of Fractional Power Encoding are primarily designed for binding operations such as circular convolution or complex-valued multiplication, which limits their compatibility with Hadamard product binding of real-valued vectors. This paper develops real-valued position encodings motivated by Random Fourier Features, aiming to retain the...
| Authors | Kenny Schlegel, Dmitri A. Rachkovskij, Denis Kleyko et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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