Orthogonal Transforms in Neural Networks Amount to Effective Regularization

Krzysztof Zając; Wojciech Sopot; Paweł Wachel

doi:10.1007/978-3-031-61857-4_33

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arXiv · 2305.06344

Orthogonal Transforms in Neural Networks Amount to Effective Regularization

Krzysztof Zając·Wojciech Sopot·Paweł Wachel

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Abstract

We consider applications of neural networks in nonlinear system identification and formulate a hypothesis that adjusting general network structure by incorporating frequency information or other known orthogonal transform, should result in an efficient neural network retaining its universal properties. We show that such a structure is a universal approximator and that using any orthogonal transform in a proposed way implies regularization during training by adjusting the learning rate of each parameter individually. We empirically show in particular, that such a structure, using the Fourier transform, outperforms equivalent models without orthogonality support.

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Krzysztof Zając, Wojciech Sopot, Paweł Wachel. 2023-05-10. Orthogonal Transforms in Neural Networks Amount to Effective Regularization. https://doi.org/10.1007/978-3-031-61857-4_33

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