Feature Encoding

Feature encoding transforms raw data into numerical representations suitable for machine learning models, aiming to capture essential information while reducing dimensionality and noise. Current research emphasizes developing effective encoding schemes for diverse data types (images, point clouds, tabular data), exploring techniques like contrastive learning, SHAP values, and autoencoders, and integrating these with various model architectures (e.g., ResNet, GNNs, diffusion models). These advancements improve model performance, interpretability (especially through methods like one-hot encoding), and enable applications such as object detection, biometric recognition, and malware classification, while also addressing challenges like privacy preservation and access control.