Poster
in
Workshop: Bayesian Decision-making and Uncertainty: from probabilistic and spatiotemporal modeling to sequential experiment design

Latent Spatial Dirichlet Allocation

Junsouk Choi ⋅ Veerabhadran Baladandayuthapani ⋅ Jian Kang

Keywords: Spatial data analysis Latent Dirichlet allocation Multiplex imaging Gaussian process

Project Page [ OpenReview]

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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