Adaptive Robust Local Online Density Estimation for Streaming Data.

Accurate online density estimation is crucial to numerous applications that are prevalent with streaming data. Existing online approaches for density estimation somewhat lack prompt adaptability and robustness when facing concept-drifting and noisy streaming data, resulting in delayed or even deteriorated approximations. To alleviate this issue, in this work, we first propose an adaptive local online kernel density estimator (ALoKDE) for real-time density estimation on data streams. ALoKDE consists of two tightly integrated strategies: (1) a statistical test for concept drift detection and (2) an adaptive weighted local online density estimation when a drift does occur. Specifically, using a weighted form, ALoKDE seeks to provide an unbiased estimation by factoring in the statistical hallmarks of the latest learned distribution and any potential distributional changes that could be introduced by each incoming instance. A robust variant of ALoKDE, i.e., R-ALoKDE, is further developed to effectively handle data streams with varied types/levels of noise. Moreover, we analyze the asymptotic properties of ALoKDE and R-ALoKDE, and also derive their theoretical error bounds regarding bias, variance, MSE and MISE. Extensive comparative studies on various artificial and real-world (noisy) streaming data demonstrate the efficacies of ALoKDE and R-ALoKDE in online density estimation and real-time classification (with noise).