Inductive Transfer using Kernel Multitask Latent Analysis
We develop the Kernel Multitask Latent Analysis (KMLA) method for modeling many-to-many relationships between inputs and responses, and show how it can be applied to inductive transfer problems in bioseparations. KMLA performs dimensionality reduction targeted towards a multitask loss function much like Kernel Partial Least Squares (KPLS). KPLS is limited to least squares multiple regression while KMLA is a more general approach that can utilize widely-used convex loss functions for inference tasks. KMLA achieves inductive transfer between tasks by forcing the tasks to share the same latent features. In the bioseparation problem, the goal is to predict the retention times for a novel anion-chromatography system; only a few retention times are known for the target systems, while many protein retention times are known for the related systems. KMLA is used with semi-supervised loss functions that do not require that all proteins have responses for all the systems. Results are presented for both regression and ranking losses. KMLA significantly outperforms both single task KMLA and KPLS, and the existing missing response algorithm for multitask PLS extended to kernels.