Lifelong context recognition via online deep clustering
Donald C. Wunsch · 2023
Until recently, evaluating the quality of unsupervised learning was too slow and expensive. This was a major hurdle to edge-enabled AI and any situations for which computational expense is a significant requirement. Adaptive Resonance Theory has been part of the solution because it can self-correct based on unsupervised category mismatch detection and reset. This advantage can be further leveraged by the development of incremental cluster validity indices. Validity indices provide various quality measures for unsupervised learning. Converting these to incremental versions is an approach that dominates prior methods, particularly for real-time or edge computing applications. Integrating incremental measures into the machine learning architecture further enhances these cost and speed advantages.