AI 中文总结
本文提出通过对齐学习核，将协作学习视为核对齐过程，并用马氏距离扩展CLaI至多类分类，在多个数据集上提升准确率、收敛速度和校准性能。
AI 中文摘要
核方法将数据表示与决策分离，但通常需要预先选择核。我们表明，该核可以通过对齐来学习，并通过最近引入的协作学习与推理（CLaI）框架发展该框架。我们证明协作学习可以被视为一个核对齐过程，其中训练一个嵌入，使其诱导的相似性与标签导出的目标核匹配。我们还证明协作推理等价于使用Parzen窗密度估计的核贝叶斯分类。受这些观点的启发，我们通过将余弦相似性替换为学习的马氏距离来推广CLaI，并将其扩展到多类分类。在CIFAR-10、PathMNIST和SleepEDF上，马氏距离公式比基于余弦的变体提高了准确率、收敛更快，并产生更低的校准误差。辅助实验进一步支持这些联系，表明CLaI产生与高斯过程相同形式的潜在信号，同时在脓毒症预测上实现有竞争力的校准。总之，这些结果建立了一个原则性的学习核框架，统一了表示学习、核对齐和贝叶斯分类，并自然扩展到多类设置。
英文摘要
Kernel methods separate data representation from decision-making, but typically require the kernel to be chosen in advance. We show that this kernel can instead be learned by alignment, and develop the resulting framework through the recently introduced Collaborative Learning and Inference (CLaI). We show that Collaborative Learning can be viewed as a kernel alignment process, in which an embedding is trained so that its induced similarity matches a label-derived target kernel. We also prove that Collaborative Inference is equivalent to kernel Bayes classification with Parzen-window density estimation. Motivated by these perspectives, we generalise CLaI by replacing cosine similarity with a learned Mahalanobis distance and extend it to multiclass classification. On CIFAR-10, PathMNIST, and SleepEDF, the Mahalanobis formulation improves accuracy, converges faster, and yields lower calibration error than the cosine-based variant. Auxiliary experiments further support these connections, showing that CLaI produces latent signals of the same form as a Gaussian process, while achieving competitive calibration on sepsis prediction. Together, these results establish a principled learned-kernel framework that unifies representation learning, kernel alignment, and Bayesian classification, and extends naturally to the multiclass setting.
