On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations

By R. Arun, V. Suresh, C. E. Veni Madhavan, M. Murty. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2010

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MMALFM (2018, background): “, LDA) is dependent on the datasets [1, 6].”
Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017, methodology): “Arun, Suresh, Madhavan, and Murthy (2010) viewed the LDA topic model as a matrix factorization mechanism and applied the symmetric Kullback–Leibler (KL) divergence (Kullback & Leibler, 1951) on the distributions generated from topicword and document-topic matrices for finding the right number of…”
Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews (2018, background): “, LDA) is dependent on the datasets [1, 4].”
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks (2022, methodology): “- Arun_2010: Uses a density-based metric as in Arun et al (2010)105 using the topic-region distribution, the cell- topic distribution and the cell coverage.”