Generative model-assisted sample selection for interest-driven progressive visual analytics

We propose interest-driven progressive visual analytics. The core idea is to filter samples with features of interest to analysts from the given dataset for analysis. The approach relies on a generative model (GM) trained using the given dataset as the training set. The GM characteristics make it convenient to find ideal generated samples from its latent space. Then, we filter the original samples similar to the ideal generated ones to explore patterns. Our research involves two methods for achieving and applying the idea. First, we give a method to explore ideal samples from a GM’s latent space. Second, we integrate the method into a system to form an embedding-based analytical workflow. Patterns found on open datasets in case studies, results of quantitative experiments, and positive feedback from experts illustrate the general usability and effectiveness of the approach.