Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable $k$-NN graph construction   The $k$-NN graph has played a central role in increasingly popular
data-driven techniques for various learning and vision tasks; yet, finding an
efficient and effective way to construct $k$-NN graphs remains a challenge,
especially for large-scale high-dimensional data. In this paper, we propose a
new approach to construct approximate $k$-NN graphs with emphasis in:
efficiency and accuracy. We hierarchically and randomly divide the data points
into subsets and build an exact neighborhood graph over each subset, achieving
a base approximate neighborhood graph; we then repeat this process for several
times to generate multiple neighborhood graphs, which are combined to yield a
more accurate approximate neighborhood graph. Furthermore, we propose a
neighborhood propagation scheme to further enhance the accuracy. We show both
theoretical and empirical accuracy and efficiency of our approach to $k$-NN
graph construction and demonstrate significant speed-up in dealing with large
scale visual data.

14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph