Nearest Centroid

Nearest centroid classification is a simple yet powerful technique that assigns data points to the class with the closest centroid (average feature vector). Current research focuses on improving its performance in various contexts, including few-shot learning and domain adaptation, often by optimizing the underlying feature representations using techniques like kernel methods or transformer-based embeddings. This renewed interest stems from its inherent interpretability and surprising effectiveness, particularly in scenarios with limited data or significant domain shifts, leading to advancements in areas such as image recognition and continual learning. The simplicity and efficiency of nearest centroid methods also make them attractive for resource-constrained applications like real-time embedded systems.