Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-supervised Nonnegative Matrix Factorization for Document
  Classification   We propose new semi-supervised nonnegative matrix factorization (SSNMF)
models for document classification and provide motivation for these models as
maximum likelihood estimators. The proposed SSNMF models simultaneously provide
both a topic model and a model for classification, thereby offering highly
interpretable classification results. We derive training methods using
multiplicative updates for each new model, and demonstrate the application of
these models to single-label and multi-label document classification, although
the models are flexible to other supervised learning tasks such as regression.
We illustrate the promise of these models and training methods on document
classification datasets (e.g., 20 Newsgroups, Reuters).

0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int