The trace norm constrained matrix-variate Gaussian process for multitask bipartite ranking

TL;DR

This paper introduces a hierarchical multitask bipartite ranking model using a trace norm constrained matrix-variate Gaussian process, improving scalability and performance in gene-disease association prediction tasks.

Contribution

It develops a novel trace constrained variational inference method for matrix-variate Gaussian processes, enabling low-rank solutions and joint convex optimization for multitask bipartite ranking.

Findings

The model performs well on real-world gene-disease datasets.

Low rank solutions enhance computational scalability.

The approach outperforms baseline models in ranking accuracy.

Abstract

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.