Nonparametric factor analysis with beta process priors

By John William Paisley, Lawrence Carin. International Conference on Machine Learning, 2009

321 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ade741995dd4c20edb0225a721ddf179c58fde05

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Citations in the corpus

A General Method for Robust Bayesian Modeling (2015, background): “Non-exchangeable models only contain global variables, such as time series models (Rabiner, 1989; Fine et al., 1998; Fox et al., 2011; Paisley and Carin, 2009) and models for network analysis (Airoldi, 2007; Airoldi et al.”
General Latent Feature Models for Heterogeneous Datasets (2017, unknown): “Hinton, 1996), nonparametric mixtures of factor analyzers (Görür and Rasmussen, 2009) and nonparametric factor analyzers with Beta process priors (Paisley and Carin, 2009).”
Variational Bayesian Inference with Stochastic Search (2012, methodology): “It has been applied to many problem domains, for example mixture modeling (Blei & Jordan, 2006), sequential modeling (Beal, 2003) and factor analysis (Paisley & Carin, 2009).”
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, methodology): “Another approach is to use sparsity [77]– [79].”
Learning the Structure of Deep Convolutional Networks (2015, methodology): “Fortunately, model structure learning has been elegantly explored by several generative model priors, such as the Indian Buffet Process (IBP) [9, 1] or Beta Process (BP) [21].”
Subspace Clustering (2011, methodology): “One way to address this issue is to put a prior on these parameters, as shown in [51].”
A Deep Generative Deconvolutional Image Model (2015, methodology): “Chen et al. (2011, 2013) trained each layer separately, sequentially, with no ﬁnal coupling of the overall model (signiﬁcantly under-mining classiﬁcation performance).”
Stochastic variational inference (2012, background): “…factorization models (Spearman, 1904; Tipping and Bishop, 1999; Collins et al., 2002; Wang, 2006; Salakhutdinov and Mnih, 2008; Paisley and Carin, 2009; Hoffman et al., 2010b), certain Bayesian nonparametric mixture models (Antoniak, 1974; Escobar and West, 1995; Teh et al., 2006a), and…”
Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images (2011, background): “Hence, for data , there is an associated set of latent binary vectors , and the beta-Bernoulli process provides a convenient prior for these vectors [29], [37], [42].”
Decomposing Spatiotemporal Brain Patterns into Topographic Latent Sources (2014, unknown): “For example, we have explored using the beta process (Paisley and Carin, 2009; Thibaux and Jordan, 2007) as a prior on Ws.”
A Tutorial on Bayesian Nonparametric Models (2011, background): “7 for an example) and IBP latent factor models (Doshi-Velez et al., 2009; Paisley et al., 2010).”
Flexible Modeling of Latent Task Structures in Multitask Learning (2012, methodology): “Our mixture of factor analyzers based MTL model uses the DP to model the mixture components so we do not need to specify their number a priori.”