Discovering Transforms: A Tutorial on Circulant Matrices, Circular Convolution, and the Discrete Fourier Transform
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Abstract (arXiv)
How could the Fourier and other transforms be naturally discovered if one didn't know how to postulate them? In the case of the Discrete Fourier Transform (DFT), we show how it arises naturally out of analysis of circulant matrices. In particular, the DFT can be derived as the change of basis that simultaneously diagonalizes all circulant matrices. In this way, the DFT arises naturally from a linear algebra question about a set of matrices. Rather than thinking of the DFT as a signal transform, it is more natural to think of it as a single change of basis that renders an entire set of mutually-commuting matrices into simple, diagonal forms. The DFT can then be "discovered" by solving the eigenvalue/eigenvector problem for a special element in that set. A brief outline is given of how this line of thinking can be generalized to families of linear operators, leading to the discovery of the other common Fourier-type transforms, as well as its connections with group representations theory.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 May 2018.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 0; 2024: 2; 2025: 0; 2026: 1.
8 papers.
- 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
- 2022Towards Multi-spatiotemporal-scale Generalized PDE Modelingcited by 190
- 2021DISCO: accurate Discrete Scale Convolutionscited by 35
- 2021Functional Regularization for Reinforcement Learning via Learned Fourier Featurescited by 17
- 2024Parameter-Efficient Fine-Tuning via Circular Convolutioncited by 4
- 2022FastRPB: a Scalable Relative Positional Encoding for Long Sequence Taskscited by 1
- 2024FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizerscited by 1
- 2026Pixel-Translation-Equivariant Quantum Convolutional Neural Networks via Fourier Multiplexerscited by 0
- 2026Pixel-Translation-Equivariant Quantum Convolutional Neural Networks via Fourier Multiplexers7 Apr 2026
- 2024FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizers9 Aug 2024
- 2024Parameter-Efficient Fine-Tuning via Circular Convolution27 Jul 2024
- 2022Towards Multi-spatiotemporal-scale Generalized PDE Modeling30 Sep 2022
- 2022FastRPB: a Scalable Relative Positional Encoding for Long Sequence Tasks23 Feb 2022
- 2021Functional Regularization for Reinforcement Learning via Learned Fourier Features6 Dec 2021
- 2021DISCO: accurate Discrete Scale Convolutions4 Jun 2021
- 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges27 Apr 2021