Coordinate Matrix Machine: A Human-level Concept Learning to Classify Very Similar Documents

Human-level concept learning argues that humans typically learn new concepts from a single example, whereas machine learning algorithms typically require hundreds of samples to learn a single concept. Our brain subconsciously identifies important features and learns more effectively. Contribution: In this paper, we introduce Coordinate Matrix Machine (CM 2 ), a structureaware document intelligence model designed to bridge the gap between semantic content and spatial layout. Unlike traditional NLP approaches that prioritize sequential text, CM 2 is particularly effective for low-data, layout-sensitive tasks, enabling high-precision performance when structural geometry is paramount. While modern “Red AI” trends rely on massive pre-training and energy-intensive GPU infrastructure, CM 2 is designed as a Green AI solution. It achieves human-level concept learning by identifying only the structural “important features” a human would consider, allowing it to classify very similar documents using only one sample per class. Advantage: Our algorithm outperforms traditional vectorizers and complex deep learning models that require larger datasets and significant compute. By focusing on structural coordinates rather than exhaustive semantic vectors, CM 2 offers: 1. High accuracy with minimal data (one-shot learning) 2. Geometric and structural intelligence 3. Green AI and environmental sustainability 4. Optimized for CPU-only environments 5. Inherent explainability (glass-box model) 6. Faster computation and low latency 7. Robustness against unbalanced classes 8. Economic viability 9. Generic, expandable, and extendable Source Code: The source code for the Python implementation of CM 2 is available on GitHub Repository.