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• W2260672489 abstract "We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables. Our algorithm assumes that every latent variable has an anchor, an observed variable with only that latent variable as its parent. Given such anchors, we show that it is possible to consistently recover moments of the latent variables and use these moments to learn complete models. We also introduce a new technique for improving the robustness of method-of-moment algorithms by optimizing over the marginal polytope or its relaxations. We evaluate our algorithm using two real-world tasks, tag prediction on questions from the Stack Overflow website and medical diagnosis in an emergency department." @default.
• W2260672489 created "2016-06-24" @default.
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• W2260672489 date "2015-11-10" @default.
• W2260672489 modified "2023-09-27" @default.
• W2260672489 title "Anchored Discrete Factor Analysis" @default.
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• W2260672489 hasPublicationYear "2015" @default.
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