Incremental Learning Based on Growing Gaussian Mixture Models

Incremental learning aims at equipping data-driven systems with self-monitoring and self-adaptation mechanisms to accommodate new data in an online setting. The resulting model underlying the system can be adjusted whenever data become available. The present paper proposes a new incremental learning algorithm, called 2G2M, to learn Growing Gaussian Mixture Models. The algorithm is furnished with abilities (1) to accommodate data online, (2) to maintain low complexity of the model, and (3) to reconcile labeled and unlabeled data. To discuss the efficiency of the proposed incremental learning algorithm, an empirical evaluation is provided.