Gradient Boosted Filters For Signal Processing
This work addresses a domain-specific problem in signal processing, offering a novel method for dynamic data but appears incremental as it adapts existing gradient boosting concepts to a new context.
The paper tackles the underexplored application of gradient boosted models to signal processing by introducing gradient boosted filters using Hammerstein systems, demonstrating effective generalizability with examples.
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.