Adaptive Whitening

Adaptive whitening is a signal processing technique aiming to decorrelate and normalize data, improving the efficiency and performance of subsequent analyses or machine learning models. Current research focuses on developing robust whitening methods that address challenges like class imbalance in classification tasks and adapting to dynamic or heterogeneous data, often employing techniques like ZCA whitening, gain modulation, and multi-timescale models within neural networks or linear autoencoders. These advancements are impacting diverse fields, including image classification, recommendation systems, hyperspectral image processing, and medical image segmentation, by enhancing model accuracy and robustness. The development of efficient and adaptable whitening algorithms continues to be a significant area of investigation.