Kernel Matrix Factorization

Kernel matrix factorization is a technique used to decompose complex data relationships into simpler, lower-dimensional representations, primarily aiming to improve efficiency and performance in various machine learning tasks. Current research focuses on developing efficient algorithms, such as asymmetric factorization and adaptive weighting schemes, to handle large-scale datasets and multiple data sources (multi-view learning). These advancements are proving valuable in diverse applications, including clustering for Alzheimer's disease analysis and improving the efficiency of convolutional neural networks for video action recognition and federated learning across heterogeneous datasets.