An adaptive estimation method with exploration and exploitation modes for non-stationary environments

• Published in: Pattern recognition Vol. 129; p. 108702
• Main Authors: Coşkun, Kutalmış, Tümer, Borahan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2022
• Subjects: Change detection; Concept drift; Dynamic learning rate; Parameter estimation; Stochastic learning; Parameter estimation; Stochastic learning; Concept drift; Change detection; Dynamic learning rate
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2022.108702

•Estimates obtained via multiplicative updates are consistent with diffusion.•Stationarity of estimates is a promising indicator for learning progress.•Utilization of a dynamic learning rate in learning under concept drift is beneficial. Dynamic systems are highly complex and hard to deal with due to their subject- and time-varying nature. The fact that most of the real world systems/events are of dynamic character makes modeling and analysis of such systems inevitable and charmingly useful. One promising estimation method that is capable of unlearning past information to deal with non-stationarity is Stochastic Learning Weak Estimator (SLWE) by Oommen and Rueda (2006). However, due to using a constant learning rate, it faces a trade-off between plasticity and stability. In this paper, we model SLWE as a random walk and provide rigorous theoretical analysis of asymptotic behavior of estimates to obtain a statistical model. Utilizing this model, we detect changes in stationarity to switch between exploratory and exploitative learning modes. Experimental evaluations on both synthetic and real world data show that the proposed method outperforms related algorithms in different types of drifts.