Exact and general decoupled solutions of the LMC Multitask Gaussian Process model

• URL: http://arxiv.org/abs/2310.12032v2
• Date: Thu, 21 Mar 2024 14:36:26 GMT
• Title: Exact and general decoupled solutions of the LMC Multitask Gaussian Process model
• Authors: Olivier Truffinet, Karim Ammar, Jean-Philippe Argaud, Bertrand Bouriquet,
• Abstract summary: The Linear Model of Co-regionalization (LMC) is a very general model of multitask gaussian process for regression or classification. Recent work has shown that under some conditions the latent processes of the model can be decoupled, leading to a complexity that is only linear in the number of said processes. We here extend these results, showing from the most general assumptions that the only condition necessary to an efficient exact computation of the LMC is a mild hypothesis on the noise model.
• Score: 28.32223907511862
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Linear Model of Co-regionalization (LMC) is a very general model of multitask gaussian process for regression or classification. While its expressivity and conceptual simplicity are appealing, naive implementations have cubic complexity in the number of datapoints and number of tasks, making approximations mandatory for most applications. However, recent work has shown that under some conditions the latent processes of the model can be decoupled, leading to a complexity that is only linear in the number of said processes. We here extend these results, showing from the most general assumptions that the only condition necessary to an efficient exact computation of the LMC is a mild hypothesis on the noise model. We introduce a full parametrization of the resulting \emph{projected LMC} model, and an expression of the marginal likelihood enabling efficient optimization. We perform a parametric study on synthetic data to show the excellent performance of our approach, compared to an unrestricted exact LMC and approximations of the latter. Overall, the projected LMC appears as a credible and simpler alternative to state-of-the art models, which greatly facilitates some computations such as leave-one-out cross-validation and fantasization.

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