Abstract: We discuss a novel, human-interpretable machine learning technique for knowledge discovery and nearest-neighbor classification. This non-neural network technique is called information lattice learning, and draws on mathematical techniques from information theory and group theory. We show how it achieves state-of-the-art performance in image classification in the regime of limited training data per class, and also how it can applied to geospatial data.
