Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Anchored Discrete Factor Analysis   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.

18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat