Gradient Boosted Filters For Signal Processing

Name: Gradient Boosted Filters For Signal Processing
Keywords: FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (cs.LG)

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2024Embargo end date: 01 Jan 2024Publisher:arXiv

Authors: Lopez, Jose A.; Stemmer, Georg; Cordourier, Hector A.;

doi: 10.48550/arxiv.2405.09305

arXiv: http://arxiv.org/abs/2405.09305

Gradient Boosted Filters For Signal Processing

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Abstract

Gradient boosted decision trees have achieved remarkable success in several domains, particularly those that work with static tabular data. However, the application of gradient boosted models to signal processing is underexplored. In this work, we introduce gradient boosted filters for dynamic data, by employing Hammerstein systems in place of decision trees. We discuss the relationship of our approach to the Volterra series, providing the theoretical underpinning for its application. We demonstrate the effective generalizability of our approach with examples.

9 pages, 12 figures. Submitted to ICML 2024 and subsequently rejected for insufficient evaluation

Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (cs.LG)

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