Feature Centroid

Feature centroids, representing the central tendency of data points within a feature space, are crucial for various machine learning tasks, primarily focusing on improving model accuracy and interpretability. Current research explores centroid applications in diverse areas, including clustering algorithms (like k-means and fuzzy c-means), noisy label handling, and backdoor attack detection in deep learning models, often leveraging U-Net architectures or novel loss functions based on centroid distances. This work significantly impacts fields like biomedical data analysis, image processing, and robust model training by enhancing data visualization, mitigating the effects of noisy data, and improving the security of machine learning systems.