This paper discusses the effect of hubness in zero-shot learning, when ridge
regression is used to find a mapping between the example space to the label
space. Contrary to the existing approach, which attempts to find a mapping from
the example space to the label space, we show that mapping labels into the
example space is desirable to suppress the emergence of hubs in the subsequent
nearest neighbor search step. Assuming a simple data model, we prove that the
proposed approach indeed reduces hubness. This was verified empirically on the
tasks of bilingual lexicon extraction and image labeling: hubness was reduced
with both of these tasks and the accuracy was improved accordingly.Comment: To be presented at ECML/PKDD 201