General Latent Feature Modeling for Data Exploration Tasks
This work addresses data exploration tasks for researchers and analysts dealing with heterogeneous data, offering an incremental improvement in interpretability and efficiency over existing methods.
The paper tackles the problem of exploratory analysis of heterogeneous datasets with mixed variable types by introducing a Bayesian nonparametric latent feature model that automatically infers model complexity and provides interpretable binary latent features, achieving linear-time inference with respect to data size.
This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables. The proposed model presents several important properties. First, it accounts for heterogeneous data while can be inferred in linear time with respect to the number of objects and attributes. Second, its Bayesian nonparametric nature allows us to automatically infer the model complexity from the data, i.e., the number of features necessary to capture the latent structure in the data. Third, the latent features in the model are binary-valued variables, easing the interpretability of the obtained latent features in data exploration tasks.