多任务二部排序的迹范数约束矩阵变量高斯过程
The trace norm constrained matrix-variate Gaussian process for multitask bipartite ranking
摘要：
我们提出了一种新的多任务二分排序的层次模型。该方法结合了矩阵变量高斯过程与生成模型的任务明智的二分排名。此外，我们采用了一种新的迹约束变分推理方法，对后验矩阵变量高斯过程施加低秩结构。由此产生的后验协方差函数推导出封闭的形式，后验均值函数是一个矩阵变量回归与一个新的谱弹性网络正则化的解决方案。此外，我们证明了迹约束矩阵变量高斯过程的变分推断与二分排序模型的最大似然参数估计相结合是联合凸的。我们的激励应用程序是候选疾病基因的优先级。这项任务的目标是帮助识别人类基因和疾病之间的未观察到的关联，使用一小部分观察到的关联，以及由基因-基因相互作用网络和疾病本体引起的内核。我们的实验结果说明了所提出的模型在现实世界的数据集上的性能。此外，我们发现，与基线模型相比，由此产生的低秩解决方案提高了训练和测试的计算可扩展性。
英文摘要：
We propose a novel hierarchical model for multitask bipartite ranking. The proposed approach combines a matrix-variate Gaussian process with a generative model for task-wise bipartite ranking. In addition, we employ a novel trace constrained variational inference approach to impose low rank structure on the posterior matrix-variate Gaussian process. The resulting posterior covariance function is derived in closed form, and the posterior mean function is the solution to a matrix-variate regression with a novel spectral elastic net regularizer. Further, we show that variational inference for the trace constrained matrix-variate Gaussian process combined with maximum likelihood parameter estimation for the bipartite ranking model is jointly convex. Our motivating application is the prioritization of candidate disease genes. The goal of this task is to aid the identification of unobserved associations between human genes and diseases using a small set of observed associations as well as kernels induced by gene-gene interaction networks and disease ontologies. Our experimental results illustrate the performance of the proposed model on real world datasets. Moreover, we find that the resulting low rank solution improves the computational scalability of training and testing as compared to baseline models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
