  Zero-shot learning provides models for targets for which instances are not
available, commonly called unobserved targets. The availability of target side
information becomes crucial in this context in order to properly induce models
for these targets. The literature is plenty of strategies to cope with this
scenario, but specifically designed on the basis of a zero-shot classification
scenario, mostly in computer vision and image classification, but they are
either not applicable or easily extensible for a zero-shot regression framework
for which a continuos value is required to be predicted rather than a label. In
fact, there is a considerable lack of methods for zero-shot regression in the
literature. Two approaches for zero-shot regression that work in a two-phase
procedure were recently proposed. They first learn the observed target models
through a classical regression learning ignoring the target side information.
Then, they aggregate those observed target models afterwards exploiting the
target side information and the models for the unobserved targets are induced.
Despite both have shown quite good performance because of the different
treatment they grant to the common features and to the side information, they
exploit features and side information separately, avoiding a global
optimization for providing the unobserved target models. The proposal of this
paper is a novel method that jointly takes features and side information in a
one-phase learning process, but treating side information properly and in a
more deserving way than as common features. A specific kernel that properly
merges features and side information is proposed for this purpose resulting in
a novel approach that exhibits better performance over both artificial and real
datasets.
