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.

翻译：对偶学习是一种专注于预测对象对结果的特殊监督学习形式。本文提出SPaiK——一种针对对偶场景设计的新型可扩展核学习方法。该方法在显著降低计算与存储需求的同时，保留了核方法的表达能力。其核心创新在于随机广义vec技巧（sGVT），这是稀疏Kronecker积乘法算法的随机扩展，能够实现大规模对偶核的高效训练。通过整合sGVT，SPaiK使得基于核的对偶学习可应用于此前难以企及的大规模数据集。我们在七个真实药物-靶标亲和力数据集上评估了SPaiK的性能，并与当前最优的对偶学习方法进行了比较。