Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Similarity and Distance Learning via Decision Forests   Canonical distances such as Euclidean distance often fail to capture the
appropriate relationships between items, subsequently leading to subpar
inference and prediction. Many algorithms have been proposed for automated
learning of suitable distances, most of which employ linear methods to learn a
global metric over the feature space. While such methods offer nice theoretical
properties, interpretability, and computationally efficient means for
implementing them, they are limited in expressive capacity. Methods which have
been designed to improve expressiveness sacrifice one or more of the nice
properties of the linear methods. To bridge this gap, we propose a highly
expressive novel decision forest algorithm for the task of distance learning,
which we call Similarity and Metric Random Forests (SMERF). We show that the
tree construction procedure in SMERF is a proper generalization of standard
classification and regression trees. Thus, the mathematical driving forces of
SMERF are examined via its direct connection to regression forests, for which
theory has been developed. Its ability to approximate arbitrary distances and
identify important features is empirically demonstrated on simulated data sets.
Last, we demonstrate that it accurately predicts links in networks.

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The categories of this paper are: solv-int
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
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The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: math
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The categories of this paper are: supr-con
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex