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Latent Dirichlet allocation (LDA) is a widely-used topic model where a set of hidden topics is learned to model a collection of data in the form of bag-of-words. Correlated topic model (CTM) extends LDA, modelling topic correlation by using logistic normal prior for topic proportion vectors, instead of Dirichlet prior. However, in the case of bag-of-words from multiple data sources (for instance,...
Latent Dirichlet allocation (LDA) is a generative probabilistic model of discrete data, where each observed item is represented as a finite mixture over latent topics. Several multi-modal extensions of LDA to model annotated data are available for image annotation. Most of existing methods model the joint distribution of image features and caption texts, in order to capture statistical correlations...
Multi-modal topic models are probabilistic generative models where hidden topics are learned from data of different types. In this paper we present supervised multi-modal latent Dirichlet allocation (smmLDA), where we incorporate class label (global description) into the joint modeling of visual words and caption words (local description), for image annotation task. We derive variational inference...
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