Bayesian learning of feature spaces for multitask regression

This paper introduces a novel approach to learn multi-task regression models with constrained architecture complexity. The proposed model, named RFF-BLR, consists of a randomised feedforward neural network with two fundamental characteristics: a single hidden layer whose units implement the random Fourier features that approximate an RBF kernel, and a Bayesian formulation that optimises the weights connecting the hidden and output layers. The RFF-based hidden layer inherits the robustness of kernel methods. The Bayesian formulation enables promoting multioutput sparsity: all tasks interplay during the optimisation to select a compact subset of the hidden layer units that serve as common non-linear mapping for every tasks. The experimental results show that the RFF-BLR framework can lead to significant performance improvements compared to the state-of-the-art methods in multitask nonlinear regression, especially in small-sized training dataset scenarios.

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Bayesian regression, Extreme learning machine, Kernel methods, Multitask regression, Random fourier features, Random vector functional link networks

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10.1016/j.neunet.2024.106619

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Sevilla-Salcedo, C, Gallardo-Antolín, A, Gómez-Verdejo, V & Parrado-Hernández, E 2024, ' Bayesian learning of feature spaces for multitask regression ', Neural Networks, vol. 179, 106619, pp. 1-16 . https://doi.org/10.1016/j.neunet.2024.106619

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https://urn.fi/URN:NBN:fi:aalto-202409046195

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[article-cris] Perustieteiden korkeakoulu / SCI

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Bayesian learning of feature spaces for multitask regression

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