Enabling feature-level interpretability in non-linear latent variable models: a synthesis of statistical and machine learning techniques - Martens, K (2019)
Gaining insights into complex high-dimensional data is challenging and typically requires the use of dimensionality reduction methods. These methods let us identify low-dimensional structures embedded within the data that may reveal patterns of interest. In probabilistic models, such low-dimensional structures are captured via latent variables.