The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling

By Sinead Williamson, Chong Wang, Katherine A. Heller, David M. Blei. 2010

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Citations in the corpus

The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification (2014, background): “, [12, 32, 13, 23, 15]); and prototype- or case-based classifiers (e.”
Graph-Sparse LDA: A Topic Model with Structured Sparsity (2014, methodology): “We focus on LIDA because it subsumes two other popular sparse topic models, the focused topic model (Williamson et al. 2010) and sparse topic model (Wang and Blei 2009), and because the proposed model is a generalization of LIDA.”
Scalable inference of topic evolution via models for latent geometric structures (2018, methodology): “[27] used Indian Buffet Process in topic models to sparsify document topic proportions.”
Streaming dynamic and distributed inference of latent geometric structures (2018, methodology): “me series modeling to capture switching regimes of an autoregressive process, where the corresponding Indian Buffet Process was used to select subsets of the latent states of the Hidden Markov Model. Williamson et al. (2010) used Indian Buffet Process in topic models to sparsify document topic propo”
General Latent Feature Models for Heterogeneous Datasets (2017, methodology): “MCMC methods have been broadly applied to infer the IBP matrix (Griffiths and Ghahramani, 2011; Williamson et al., 2010; Titsias, 2007).”
Sparse Additive Generative Models of Text (2011, methodology): “Williamson et al. (2010) induce sparsity in the topic proportions by using the Indian Buﬀet Process to represent the presence or absence of topics in a document.”
Beta-Negative Binomial Process and Poisson Factor Analysis (2011, methodology): “5 and shown in Table 1, NMF (Lee and Seung, 2000) is a special case of Γ-PFA; GaP (Canny, 2004) is a special case of β Γ-PFA; and in terms of inference, Dir-PFA is equivalent to LDA (Blei et al., 2003; Hoﬀman et al., 2010); and S γ Γ-PFA is closely related to FTM (Williamson et al., 2010).”
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction (2015, background): “Sparsity-inducing versions of these models [3, 4, 5, 6] make this goal more explicit by trying to limit the number of non-zero values in each factor.”
Deep Poisson Factor Modeling (2015, methodology): “It is also worth noting that the model in (1) is closely related to other widely known topic model approaches, such as Latent Dirichlet Allocation (LDA) [3], HDP [27] and Focused Topic Modeling (FTM) [29].”
Interactive and interpretable machine learning models for human machine collaboration (2015, background): “, [43, 134, 45, 90, 60]); and prototype- or case-based classifiers (e.”