Orthogonal Transforms

Orthogonal transforms are mathematical operations that preserve geometric properties, finding increasing use in diverse machine learning applications. Current research focuses on leveraging orthogonal transformations to improve model efficiency, robustness, and fairness, particularly within neural networks, employing techniques like orthogonal parameterization, and orthogonal mapping of feature spaces. These methods address challenges such as backward compatibility in model updates, semantic confusion in object detection, and bias mitigation in decision-making, leading to improved accuracy and reduced computational costs across various domains.