Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear
  Spectral Penalties   We present a general framework to learn functions in tensor product
reproducing kernel Hilbert spaces (TP-RKHSs). The methodology is based on a
novel representer theorem suitable for existing as well as new spectral
penalties for tensors. When the functions in the TP-RKHS are defined on the
Cartesian product of finite discrete sets, in particular, our main problem
formulation admits as a special case existing tensor completion problems. Other
special cases include transfer learning with multimodal side information and
multilinear multitask learning. For the latter case, our kernel-based view is
instrumental to derive nonlinear extensions of existing model classes. We give
a novel algorithm and show in experiments the usefulness of the proposed
extensions.

25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph