Light-weight Deep Extreme Multilabel Classification. (arXiv:2304.11045v1 [cs.LG])

Extreme multi-label (XML) classification refers to the task of supervised
multi-label learning that involves a large number of labels. Hence, scalability
of the classifier with increasing label dimension is an important
consideration. In this paper, we develop a method called LightDXML which
modifies the recently developed deep learning based XML framework by using
label embeddings instead of feature embedding for negative sampling and
iterating cyclically through three major phases: (1) proxy training of label
embeddings (2) shortlisting of labels for …

arxiv classification classifier deep learning embedding feature framework labels light major negative paper sampling scalability training xml