Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Collaborative Low-Rank Subspace Clustering   In this paper we present Collaborative Low-Rank Subspace Clustering. Given
multiple observations of a phenomenon we learn a unified representation matrix.
This unified matrix incorporates the features from all the observations, thus
increasing the discriminative power compared with learning the representation
matrix on each observation separately. Experimental evaluation shows that our
method outperforms subspace clustering on separate observations and the state
of the art collaborative learning algorithm.

18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph