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cs.LG2025
Stick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streams
Federica Granese, Serena Villata, Charles Bouveyron
Online topic models are unsupervised algorithms to identify latent topics in data streams that continuously evolve over time. Although these methods naturally align with real-world…
cs.LG2025
Merging Embedded Topics with Optimal Transport for Online Topic Modeling on Data Streams
Federica Granese, Benjamin Navet, Serena Villata +1
Topic modeling is a key component in unsupervised learning, employed to identify topics within a corpus of textual data. The rapid growth of social media generates an ever-growing…