Latent Variable Multi-output Gaussian Processes for Hierarchical Datasets
This work addresses the need for more elaborate correlation structures in multi-output Gaussian processes for hierarchical datasets, such as in biological experiments, but it is incremental as it extends existing methods.
The paper tackles the problem of modeling hierarchical datasets with multi-output Gaussian processes by proposing a tailored kernel that captures different correlation levels and uses latent variables to improve scalability. Experimental results on synthetic and real-world data from genomics and motion capture support the claims.
Multi-output Gaussian processes (MOGPs) have been introduced to deal with multiple tasks by exploiting the correlations between different outputs. Generally, MOGPs models assume a flat correlation structure between the outputs. However, such a formulation does not account for more elaborate relationships, for instance, if several replicates were observed for each output (which is a typical setting in biological experiments). This paper proposes an extension of MOGPs for hierarchical datasets (i.e. datasets for which the relationships between observations can be represented within a tree structure). Our model defines a tailored kernel function accounting for hierarchical structures in the data to capture different levels of correlations while leveraging the introduction of latent variables to express the underlying dependencies between outputs through a dedicated kernel. This latter feature is expected to significantly improve scalability as the number of tasks increases. An extensive experimental study involving both synthetic and real-world data from genomics and motion capture is proposed to support our claims.