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Variational learning for latent Gaussian model of discrete data

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dc.contributor.author Khan, Mohammad
dc.date.accessioned 2012-12-01T00:17:42Z
dc.date.available 2012-12-01T00:17:42Z
dc.date.copyright 2012 en_US
dc.date.issued 2012-11-30
dc.identifier.uri http://hdl.handle.net/2429/43640
dc.description.abstract This thesis focuses on the variational learning of latent Gaussian models for discrete data. The learning is difficult since the discrete-data likelihood is not conjugate to the Gaussian prior. Existing methods to solve this problem are either inaccurate or slow. We consider a variational approach based on evidence lower bound optimization. We solve the following two main problems of the variational approach: the computational inefficiency associated with the maximization of the lower bound and the intractability of the lower bound. For the first problem, we establish concavity of the lower bound and design fast learning algorithms using concave optimization. For the second problem, we design tractable and accurate lower bounds, some of which have provable error guarantees. We show that these lower bounds not only make accurate variational learning possible, but can also give rise to algorithms with a wide variety of speed-accuracy trade-offs. We compare various lower bounds, both theoretically and experimentally, giving clear design guidelines for variational algorithms. Through application to real-world data, we show that the variational approach can be more accurate and faster than existing methods. en_US
dc.language.iso eng en_US
dc.publisher University of British Columbia en
dc.title Variational learning for latent Gaussian model of discrete data en_US
dc.type Electronic Thesis or Dissertation en
dc.degree.name Doctor of Philosophy - PhD en_US
dc.degree.discipline Computer Science en_US
dc.degree.grantor University of British Columbia en
dc.date.graduation 2013-05 en_US
dc.degree.campus UBCV en_US
dc.description.scholarlevel Graduate en


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