Kernel Learning

Kernel learning aims to leverage the power of kernel methods, which map data into higher-dimensional spaces to capture non-linear relationships, for improved machine learning performance. Current research focuses on developing efficient and scalable algorithms for multiple kernel learning (MKL), often employing techniques like graph embeddings, matrix factorization, and ADMM optimization to handle large datasets and multiple kernel types. These advancements are impacting diverse fields, including bioinformatics (multi-omics data integration), medical image analysis (segmentation), and distributed learning (federated settings), by enabling more accurate and efficient models for complex data.