An Automatic Type-Inferential General Latent Feature Model

With ever more data becoming available, there has been a recent drive to develop modelling tools for heterogeneous data sets such as electronic healthcare records. Therein appears both Bayesian non-parametric latent feature models, as well as methods for automatically determining the statistical data type (e.g. ordinal or categorical) of the attributes present in the data. We present a model which combines both of these attractive features in an end-to-end framework. By jointly learning the model complexity and statistical type of the data, we demonstrate that redundant information can be discarded while higher accuracy statistical type estimates are produced on real experimental data.