One-pass online learning under evolving feature data streams: A non-parametric model

Abstract

In real-world applications, data streams naturally evolves and thus may exhibit a dynamic feature space, wherein new features appear and old ones disappear. Online learning under such circumstances necessitates simultaneous learning from increasing data volume and adaptation to the dynamic feature space in real time. While several methodologies have been proposed to tackle this challenge, many of them rely on strong assumptions regarding evolving interaction manners. Instead, this study adopts a broader perspective aimed at facilitating learning from arbitrarily evolving features without any strict assumptions in the previous work. Then, we present an online learning method based on a non-parametric kernel model. This model accommodates data streams with both continuous instances and evolving features through simple deduction and addition operations. Theoretical analysis shows the sublinear regret $O\left(\sqrt{T}\right)$ of the proposed method. Empirical studies show the capability to adapt not only to the previously constrained evolving features but also to the more arbitrarily evolving features.