Unification of Signal Transform Theory

Abstract

We unify the discrete Fourier transform (DFT), discrete cosine transform (DCT), Walsh-Hadamard, Haar wavelet, Karhunen-Loève transform (KLT), and several others along with their continuous counterparts (Fourier transform, Fourier series, spherical harmonics, fractional Fourier transform) under one representation-theoretic principle: each is the eigenbasis of every covariance invariant under a specific finite or compact group, with columns constructed from the irreducible matrix elements of the group via the Peter-Weyl theorem. The unification rests on the Algebraic Diversity (AD) framework, which identifies the matched group of a covariance as the foundational object of second-order signal processing. The data-dependent KLT emerges as the trivial-matched-group limit; classical transforms emerge as the cyclic, dihedral, elementary Abelian, iterated wreath, and hybrid wreath cases, with composition rules for direct, wreath, and semidirect products. The dihedral case is split by action: the Hartley basis is the matched transform of the dihedral group on the M-cycle (symmetric circulants), while the DCT belongs to the dihedral group on the even-reflected extension, with the finite-window AR(1) covariance an approximation to that class. A polynomial-time algorithm, the DAD-CAD relaxation cast as a double-commutator generalized eigenvalue problem, discovers the matched group of any empirical covariance without expert judgment, with noise-aware variants via the commutativity residual δ and algebraic coloring index α. The fractional Fourier transform is treated as the metaplectic SO(2) case, and a structural principle relates matched group size inversely to transform resolution. Modern applications (massive-MIMO, graph neural networks, transformer attention, 3D vision, brain connectivity, single-cell genomics, quantum informatics) are sketched with their matched groups.

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Cite

BibTeX RIS

BibTeX

@misc{thornton2026unification,
  title = {{Unification of Signal Transform Theory}},
  author = {Mitchell A. Thornton},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2605.11589},
  eprint = {2605.11589},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2605.11589},
}

RIS

TY  - UNPB
TI  - Unification of Signal Transform Theory
AU  - Mitchell A. Thornton
PY  - 2026
DA  - 2026/09/10
PB  - arXiv
DO  - 10.48550/arxiv.2605.11589
UR  - https://arxiv.org/abs/2605.11589
AB  - We unify the discrete Fourier transform (DFT), discrete cosine transform (DCT), Walsh-Hadamard, Haar wavelet, Karhunen-Loève transform (KLT), and several others along with their continuous counterparts (Fourier transform, Fourier series, spherical harmonics, fractional Fourier transform) under one representation-theoretic principle: each is the eigenbasis of every covariance invariant under a specific finite or compact group, with columns constructed from the irreducible matrix elements of the group via the Peter-Weyl theorem. The unification rests on the Algebraic Diversity (AD) framework, which identifies the matched group of a covariance as the foundational object of second-order signal processing. The data-dependent KLT emerges as the trivial-matched-group limit; classical transforms emerge as the cyclic, dihedral, elementary Abelian, iterated wreath, and hybrid wreath cases, with composition rules for direct, wreath, and semidirect products. The dihedral case is split by action: the Hartley basis is the matched transform of the dihedral group on the M-cycle (symmetric circulants), while the DCT belongs to the dihedral group on the even-reflected extension, with the finite-window AR(1) covariance an approximation to that class. A polynomial-time algorithm, the DAD-CAD relaxation cast as a double-commutator generalized eigenvalue problem, discovers the matched group of any empirical covariance without expert judgment, with noise-aware variants via the commutativity residual δ and algebraic coloring index α. The fractional Fourier transform is treated as the metaplectic SO(2) case, and a structural principle relates matched group size inversely to transform resolution. Modern applications (massive-MIMO, graph neural networks, transformer attention, 3D vision, brain connectivity, single-cell genomics, quantum informatics) are sketched with their matched groups.
ER  -

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