Dynamic Kernel Selection for Adaptive Learning in Sparsity-Constrained Environments

Abstract

In the rapidly evolving landscape of machine learning, the capability to process and learn from data streams in real-time is becoming increasingly vital across numerous domains, including finance, healthcare, and sensor network monitoring. These applications often grapple with the challenge of sparsity-constrained data streams, where only a small subset of features or data points holds significant information at any given time. This sparsity presents a unique set of challenges, necessitating sophisticated learning algorithms that can adaptively select and process the most informative features to make accurate predictions or decisions.