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• W1912587717 abstract "We introduce a Bayesian model for inferring mixtures of subspaces of different dimensions. The key challenge in such a mixture model is specification of prior distributions over subspaces of different dimensions. We address this challenge by embedding subspaces or Grassmann manifolds into a sphere of relatively low dimension and specifying priors on the sphere. We provide an efficient sampling algorithm for the posterior distribution of the model parameters. We illustrate that a simple extension of our mixture of subspaces model can be applied to topic modeling. We also prove posterior consistency for the mixture of subspaces model. The utility of our approach is demonstrated with applications to real and simulated data." @default.
• W1912587717 created "2016-06-24" @default.
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• W1912587717 date "2014-04-27" @default.
• W1912587717 modified "2023-09-27" @default.
• W1912587717 title "Learning Subspaces of Different Dimension" @default.
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• W1912587717 hasPublicationYear "2014" @default.
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