Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Local Similarities, Global Coding: An Algorithm for Feature Coding and
  its Applications   Data coding as a building block of several image processing algorithms has
been received great attention recently. Indeed, the importance of the locality
assumption in coding approaches is studied in numerous works and several
methods are proposed based on this concept. We probe this assumption and claim
that taking the similarity between a data point and a more global set of anchor
points does not necessarily weaken the coding method as long as the underlying
structure of the anchor points are taken into account. Based on this fact, we
propose to capture this underlying structure by assuming a random walker over
the anchor points. We show that our method is a fast approximate learning
algorithm based on the diffusion map kernel. The experiments on various
datasets show that making different state-of-the-art coding algorithms aware of
this structure boosts them in different learning tasks.

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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol