Orthogonal Random Feature

Orthogonal random features are a technique used to efficiently approximate kernel methods, improving scalability and reducing computational costs in machine learning. Current research focuses on applying this technique within various model architectures, including transformers and neural networks, to enhance performance in diverse tasks such as image dehazing, video recognition, and facial expression recognition. This approach leverages the benefits of orthogonal projections to create more informative and robust feature representations, leading to improved accuracy and efficiency in numerous applications. The resulting improvements in model performance and computational efficiency have significant implications for various fields, including computer vision and natural language processing.