Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Beyond $1/2$-Approximation for Submodular Maximization on Massive Data
  Streams   Many tasks in machine learning and data mining, such as data diversification,
non-parametric learning, kernel machines, clustering etc., require extracting a
small but representative summary from a massive dataset. Often, such problems
can be posed as maximizing a submodular set function subject to a cardinality
constraint. We consider this question in the streaming setting, where elements
arrive over time at a fast pace and thus we need to design an efficient,
low-memory algorithm. One such method, proposed by Badanidiyuru et al. (2014),
always finds a $0.5$-approximate solution. Can this approximation factor be
improved? We answer this question affirmatively by designing a new algorithm
SALSA for streaming submodular maximization. It is the first low-memory,
single-pass algorithm that improves the factor $0.5$, under the natural
assumption that elements arrive in a random order. We also show that this
assumption is necessary, i.e., that there is no such algorithm with better than
$0.5$-approximation when elements arrive in arbitrary order. Our experiments
demonstrate that SALSA significantly outperforms the state of the art in
applications related to exemplar-based clustering, social graph analysis, and
recommender systems.

11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
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The categories of this paper are: eess
2
The categories of this paper are: math