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Algebraic Diversity: Group-Theoretic Spectral Estimation from Single Observations
arXiv:2604.03634v2 Announce Type: replace Abstract: We establish that temporal averaging over multiple observations is the degenerate case of algebraic group action with the trivial group G={e}. A
arXiv:2604.03634v2 Announce Type: replace Abstract: We establish that temporal averaging over multiple observations is the degenerate case of algebraic group action with the trivial group G={e}. A General Replacement Theorem proves that a group-averaged estimator from one snapshot achieves equivalent subspace decomposition to multi-snapshot covariance estimation. The Trivial Group Embedding Theorem proves that the sample covariance is the accumulation of trivial-group estimates, with variance governed by a (G,L) continuum as 1/(|G|dot L). The processing gain 10log_{10}(M) dB equals the classical beamforming gain, establishing that this gain is a property of group order, not sensor count. The DFT, DCT, and KLT are unified as group-matched special cases. We conjecture a General Algebraic Averaging Theorem extending these results to arbitrary statistics, with variance governed by the effective group order d_{eff}. Monte Carlo experiments on the first four sample moments across five group types confirm the conjecture to four-digit precision. The framework exploits the structure of information (representation-theoretic symmetry of the data object) rather than the content, complementing Shannon's theory. Five applications are demonstrated: single-snapshot MUSIC, massive MIMO with 64% throughput gain, single-pulse waveform classification at 90% accuracy, graph signal processing with non-abelian groups, and algebraic analysis of transformer LLMs revealing RoPE uses the wrong group for 70--80% of attention heads (22,480 observations across five models).
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Source: arXiv cs.LG | 2026-04-10