提出新方法SPaiK，让大规模药物靶点亲和力预测更高效。
Scalable Pairwise Kernel Learning with Stochastic Vec Trick
- 用随机广义vec trick加速配对核计算
- 在7个真实数据集上实现更快训练与更低内存占用
- 适合处理超大规模配对学习任务的研究者
配对学习是一种专注于预测对象对结果的特殊监督学习。本文提出SPaiK，一种专为配对场景设计的新颖可扩展核学习方法。该方法在保持核方法表达能力的同时，显著降低计算与内存开销。核心创新是随机广义vec trick（sGVT），即稀疏克罗内克积乘法算法的随机化扩展，实现了配对核的大规模高效训练。通过引入sGVT，SPaiK使基于核的配对学习得以应用于此前难以处理的大规模数据集。我们在七个真实世界的药物-靶点亲和力数据集上评估了SPaiK的性能，并与当前最优的配对学习方法进行了对比。
原文摘要 · Abstract (English)
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.
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