Latent Spatial Dirichlet Allocation

Abstract

We propose a novel topic modeling approach, latent spatial Dirichlet allocation (LSDA), which generalizes the latent Dirichlet allocation to spatial data. LSDA integrates spatial Gaussian processes within the LDA framework, thereby effectively capturing complex spatial dependencies inherent in spatial data. We develop an efficient Markov chain Monte Carlo algorithm, and applications to both real and synthetic datasets successfully demonstrate the utility of LSDA.

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Cite

Text

Choi et al. "Latent Spatial Dirichlet Allocation." NeurIPS 2024 Workshops: BDU, 2024.

Markdown

[Choi et al. "Latent Spatial Dirichlet Allocation." NeurIPS 2024 Workshops: BDU, 2024.](https://mlanthology.org/neuripsw/2024/choi2024neuripsw-latent/)

BibTeX

@inproceedings{choi2024neuripsw-latent,
  title     = {{Latent Spatial Dirichlet Allocation}},
  author    = {Choi, Junsouk and Baladandayuthapani, Veerabhadran and Kang, Jian},
  booktitle = {NeurIPS 2024 Workshops: BDU},
  year      = {2024},
  url       = {https://mlanthology.org/neuripsw/2024/choi2024neuripsw-latent/}
}