Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Leveraging Sparsity for Efficient Submodular Data Summarization   The facility location problem is widely used for summarizing large datasets
and has additional applications in sensor placement, image retrieval, and
clustering. One difficulty of this problem is that submodular optimization
algorithms require the calculation of pairwise benefits for all items in the
dataset. This is infeasible for large problems, so recent work proposed to only
calculate nearest neighbor benefits. One limitation is that several strong
assumptions were invoked to obtain provable approximation guarantees. In this
paper we establish that these extra assumptions are not necessary---solving the
sparsified problem will be almost optimal under the standard assumptions of the
problem. We then analyze a different method of sparsification that is a better
model for methods such as Locality Sensitive Hashing to accelerate the nearest
neighbor computations and extend the use of the problem to a broader family of
similarities. We validate our approach by demonstrating that it rapidly
generates interpretable summaries.

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The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph