Fast Partial Fourier Transform

Yong-chan Park, Jun-Gi Jang, U Kang

Keywords: Fourier transform, time series, signal processing, anomaly detection, machine learning

Abstract:

Given a time-series vector, how can we efficiently compute a specified part of Fourier coefficients? Fast Fourier transform (FFT) is a widely used algorithm that computes the discrete Fourier transform in many machine learning applications. Despite the pervasive use, FFT algorithms do not provide a fine-tuning option for the user to specify one’s demand, that is, the output size (the number of Fourier coefficients to be computed) is algorithmically determined by the input size. Such a lack of flexibility is often followed by just discarding the unused coefficients because many applications do not require the whole spectrum of the frequency domain, resulting in an inefficiency due to the extra computation.
In this paper, we propose a fast Partial Fourier Transform (PFT), an efficient algorithm for computing only a part of Fourier coefficients. PFT approximates a part of twiddle factors (trigonometric constants) using polynomials, thereby reducing the computational complexity due to the mixture of many twiddle factors. We derive the asymptotic time complexity of PFT with respect to input and output sizes, as well as its numerical accuracy. Experimental results show that PFT outperforms the current state-of-the-art algorithms, with an order of magnitude of speedup for sufficiently small output sizes without sacrificing accuracy.

One-sentence Summary: We propose a fast Partial Fourier Transform (PFT), an efficient algorithm for computing only a part of Fourier coefficients.

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