Auto-Encoding Variational Bayes for Inferring Topics and Visualization
This work addresses a scalability problem for researchers and practitioners in text analysis, offering an incremental improvement over prior methods.
The paper tackles the challenge of scalability in jointly inferring topics and visualization for text analysis by introducing a fast Auto-Encoding Variational Bayes inference method, demonstrating its efficiency and effectiveness on real-world large datasets compared to existing baselines.
Visualization and topic modeling are widely used approaches for text analysis. Traditional visualization methods find low-dimensional representations of documents in the visualization space (typically 2D or 3D) that can be displayed using a scatterplot. In contrast, topic modeling aims to discover topics from text, but for visualization, one needs to perform a post-hoc embedding using dimensionality reduction methods. Recent approaches propose using a generative model to jointly find topics and visualization, allowing the semantics to be infused in the visualization space for a meaningful interpretation. A major challenge that prevents these methods from being used practically is the scalability of their inference algorithms. We present, to the best of our knowledge, the first fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization. Since our method is black box, it can handle model changes efficiently with little mathematical rederivation effort. We demonstrate the efficiency and effectiveness of our method on real-world large datasets and compare it with existing baselines.