Jun Zhu
Sparse Topical Coding.
Mon, 02/20/2012 - 15:01 — SomNambulAuthors: Jun Zhu, Eric P. Xing
Subjects: Learning
AbstractWe present sparse topical coding (STC), a non-probabilistic formulation of
topic models for discovering latent representations of large collections of
data. Unlike probabilistic topic models, STC relaxes the normalization
constraint of admixture proportions and the constraint of defining a normalized
likelihood function.MedLDA: A General Framework of Maximum Margin Supervised Topic Models.
Thu, 12/31/2009 - 16:01 — SomNambulAuthors: Jun Zhu, Amr Ahmed, Eric P. Xing
Subjects: Machine Learning
AbstractSupervised topic models utilize document's side information for discovering
predictive low dimensional representations of documents. Existing models apply
the likelihood-based estimation. In this paper, we present a general framework
of max-margin supervised topic models for both continuous and categorical
response variables. Our approach, the maximum entropy discrimination latent
Dirichlet allocation (MedLDA), utilizes the max-margin principle to train
supervised topic models and estimate predictive topic representations that are
arguably more suitable for prediction tasks.
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