Rational Gaussian wavelets and corresponding model driven neural networks
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911406391033856 |
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| author | Ámon, Attila Miklós Fenech, Kristian Kovács, Péter Dózsa, Tamás |
| author_facet | Ámon, Attila Miklós Fenech, Kristian Kovács, Péter Dózsa, Tamás |
| contents | In this paper we consider the continuous wavelet transform using Gaussian wavelets multiplied by an appropriate rational term. The zeros and poles of this rational modifier act as free parameters and their choice highly influences the shape of the mother wavelet. This allows the proposed construction to approximate signals with complex morphology using only a few wavelet coefficients. We show that the proposed rational Gaussian wavelets are admissible and provide numerical approximations of the wavelet coefficients using variable projection operators. In addition, we show how the proposed variable projection based rational Gaussian wavelet transform can be used in neural networks to obtain a highly interpretable feature learning layer. We demonstrate the effectiveness of the proposed scheme through a biomedical application, namely, the detection of ventricular ectopic beats (VEBs) in real ECG measurements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rational Gaussian wavelets and corresponding model driven neural networks Ámon, Attila Miklós Fenech, Kristian Kovács, Péter Dózsa, Tamás Machine Learning Artificial Intelligence 65D15 G.1.2 In this paper we consider the continuous wavelet transform using Gaussian wavelets multiplied by an appropriate rational term. The zeros and poles of this rational modifier act as free parameters and their choice highly influences the shape of the mother wavelet. This allows the proposed construction to approximate signals with complex morphology using only a few wavelet coefficients. We show that the proposed rational Gaussian wavelets are admissible and provide numerical approximations of the wavelet coefficients using variable projection operators. In addition, we show how the proposed variable projection based rational Gaussian wavelet transform can be used in neural networks to obtain a highly interpretable feature learning layer. We demonstrate the effectiveness of the proposed scheme through a biomedical application, namely, the detection of ventricular ectopic beats (VEBs) in real ECG measurements. |
| title | Rational Gaussian wavelets and corresponding model driven neural networks |
| topic | Machine Learning Artificial Intelligence 65D15 G.1.2 |
| url | https://arxiv.org/abs/2502.01282 |