Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Completing a joint PMF from projections: a low-rank coupled tensor
  factorization approach   There has recently been considerable interest in completing a low-rank matrix
or tensor given only a small fraction (or few linear combinations) of its
entries. Related approaches have found considerable success in the area of
recommender systems, under machine learning. From a statistical estimation
point of view, the gold standard is to have access to the joint probability
distribution of all pertinent random variables, from which any desired optimal
estimator can be readily derived. In practice high-dimensional joint
distributions are very hard to estimate, and only estimates of low-dimensional
projections may be available. We show that it is possible to identify
higher-order joint PMFs from lower-order marginalized PMFs using coupled
low-rank tensor factorization. Our approach features guaranteed identifiability
when the full joint PMF is of low-enough rank, and effective approximation
otherwise. We provide an algorithmic approach to compute the sought factors,
and illustrate the merits of our approach using rating prediction as an
example.

10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph