Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact
  Two-Step Method   Pairwise learning or dyadic prediction concerns the prediction of properties
for pairs of objects. It can be seen as an umbrella covering various machine
learning problems such as matrix completion, collaborative filtering,
multi-task learning, transfer learning, network prediction and zero-shot
learning. In this work we analyze kernel-based methods for pairwise learning,
with a particular focus on a recently-suggested two-step method. We show that
this method offers an appealing alternative for commonly-applied
Kronecker-based methods that model dyads by means of pairwise feature
representations and pairwise kernels. In a series of theoretical results, we
establish correspondences between the two types of methods in terms of linear
algebra and spectral filtering, and we analyze their statistical consistency.
In addition, the two-step method allows us to establish novel algorithmic
shortcuts for efficient training and validation on very large datasets. Putting
those properties together, we believe that this simple, yet powerful method can
become a standard tool for many problems. Extensive experimental results for a
range of practical settings are reported.

19
The categories of this paper are: eess
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys