Mixed Likelihood Gaussian Process Latent Variable Model
This work addresses a limitation in GP-LVMs for researchers and practitioners dealing with heterogeneous data types, though it is incremental as it builds on existing GP-LVM frameworks.
The paper tackled the problem of modeling data with mixed attribute types, such as categorical or nominal, by introducing a Mixed Likelihood Gaussian Process Latent Variable Model (GP-LVM) that uses separate likelihoods for each dimension, resulting in more meaningful latent representations and improved predictive performance on real-world data.
We present the Mixed Likelihood Gaussian process latent variable model (GP-LVM), capable of modeling data with attributes of different types. The standard formulation of GP-LVM assumes that each observation is drawn from a Gaussian distribution, which makes the model unsuited for data with e.g. categorical or nominal attributes. Our model, for which we use a sampling based variational inference, instead assumes a separate likelihood for each observed dimension. This formulation results in more meaningful latent representations, and give better predictive performance for real world data with dimensions of different types.