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Non-Gaussian discriminative factor models via the max-margin rank-likelihood
Abstract
Copyright © 2015 by the author(s).We consider the problem of discriminative factor
analysis for data that are in general non-Gaussian. A Bayesian model based on the
ranks of the data is proposed. We first introduce a new max-margin version of the
rank-likelihood. A discriminative factor model is then developed, integrating the
max-margin rank-likelihood and (linear) Bayesian support vector machines, which are
also built on the max-margin principle. The discriminative factor model is further
extended to the nonlinear case through mixtures of local linear classifiers, via Dirichlet
processes. Fully local conjugacy of the model yields efficient inference with both
Markov Chain Monte Carlo and variational Bayes approaches. Extensive experiments on
benchmark and real data demonstrate superior performance of the proposed model and
its potential for applications in computational biology.
Type
Journal articlePermalink
https://hdl.handle.net/10161/13121Collections
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Show full item recordScholars@Duke
Lawrence Carin
Professor of Electrical and Computer Engineering
Lawrence Carin earned the BS, MS, and PhD degrees in electrical engineering at the
University of Maryland, College Park, in 1985, 1986, and 1989, respectively. In 1989
he joined the Electrical Engineering Department at Polytechnic University (Brooklyn)
as an Assistant Professor, and became an Associate Professor there in 1994. In September
1995 he joined the Electrical and Computer Engineering (ECE) Department at Duke University,
where he is now a Professor. He was ECE Department Chair from 2011
Ricardo Henao
Associate Professor in Biostatistics & Bioinformatics
Ephraim Tsalik
Adjunct Associate Professor in the Department of Medicine
My research at Duke has focused on understanding the dynamic between host and pathogen
so as to discover and develop host-response markers that can diagnose and predict
health and disease. This new and evolving approach to diagnosing illness has the
potential to significantly impact individual as well as public health considering
the rise of antibiotic resistance.
With any potential infectious disease diagnosis, it is difficult, if not impossible,
to determine at the time of pre
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