Scalable Pairwise Kernel Learning with Stochastic Vec Trick
2026-06-15 • Machine Learning
Machine Learning
AI summaryⓘ
The authors present SPaiK, a new method to efficiently learn from pairs of objects, like drug and target pairs, using kernels, which are powerful but usually slow. They introduce a technique called stochastic generalized vec trick (sGVT) that speeds up computations and uses less memory, making it possible to handle much larger datasets. They tested SPaiK on seven real drug-target datasets and compared it to other top methods for pairwise learning.
pairwise learningsupervised learningkernel methodsstochastic algorithmsKronecker productvectorization tricksdrug-target affinitymachine learning scalabilitylarge-scale training
Authors
Napsu Karmitsa, Tapio Pahikkala, Antti Airola
Abstract
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.