Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Metric Learning for Ordered Labeled Trees with pq-grams   Computing the similarity between two data points plays a vital role in many
machine learning algorithms. Metric learning has the aim of learning a good
metric automatically from data. Most existing studies on metric learning for
tree-structured data have adopted the approach of learning the tree edit
distance. However, the edit distance is not amenable for big data analysis
because it incurs high computation cost. In this paper, we propose a new metric
learning approach for tree-structured data with pq-grams. The pq-gram distance
is a distance for ordered labeled trees, and has much lower computation cost
than the tree edit distance. In order to perform metric learning based on
pq-grams, we propose a new differentiable parameterized distance, weighted
pq-gram distance. We also propose a way to learn the proposed distance based on
Large Margin Nearest Neighbors (LMNN), which is a well-studied and practical
metric learning scheme. We formulate the metric learning problem as an
optimization problem and use the gradient descent technique to perform metric
learning. We empirically show that the proposed approach not only achieves
competitive results with the state-of-the-art edit distance-based methods in
various classification problems, but also solves the classification problems
much more rapidly than the edit distance-based methods.

36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc