Asymmetric Kernel

Asymmetric kernels, unlike their symmetric counterparts, represent relationships that are not necessarily reciprocal, finding applications in diverse fields where directional dependencies exist, such as directed graphs and conditional probabilities. Current research focuses on developing algorithms like asymmetric Kernel Singular Value Decomposition (KSVD) and adapting existing methods such as kernel ridge regression and random Fourier features to handle these kernels effectively, often involving novel approaches to feature learning and dimensionality reduction. This work is significant because it expands the applicability of kernel methods to a wider range of data types and problems, potentially improving performance in areas like natural language processing and machine learning tasks involving non-symmetric relationships.