Scalable Pairwise Kernel Learning with Stochastic Vec Trick
For researchers in drug discovery and pairwise learning, SPaiK offers a practical way to scale kernel methods to large datasets, though the improvement is incremental over existing methods.
SPaiK introduces a scalable kernel learning method for pairwise prediction, reducing computational and memory costs via the stochastic generalized vec trick. It achieves competitive performance on seven drug-target affinity datasets, enabling kernel-based pairwise learning on previously infeasible dataset sizes.
Pairwise learning is a specialized form of supervised learning that focuses on predicting outcomes for pairs of objects. In this work, we introduce SPaiK, a new scalable kernel learning method tailored for pairwise settings. Our approach preserves the expressive power of kernel methods while substantially reducing computational and memory requirements. The key innovation is the stochastic generalized vec trick (sGVT), a stochastic extension of the sparse Kronecker product multiplication algorithm, which enables efficient large-scale training with pairwise kernels. By incorporating sGVT, SPaiK makes it possible to apply kernel-based pairwise learning to datasets of a size previously out of reach. We evaluate the performance of SPaiK on seven real-world drug-target affinity datasets and compare the results with state-of-the-art methods in pairwise learning.