  Extreme multilabel classification or XML, is an active area of interest in
machine learning. Compared to traditional multilabel classification, here the
number of labels is extremely large, hence, the name extreme multilabel
classification. Using classical one versus all classification wont scale in
this case due to large number of labels, same is true for any other
classifiers. Embedding of labels as well as features into smaller label space
is an essential first step. Moreover, other issues include existence of head
and tail labels, where tail labels are labels which exist in relatively smaller
number of given samples. The existence of tail labels creates issues during
embedding. This area has invited application of wide range of approaches
ranging from bit compression motivated from compressed sensing, tree based
embeddings, deep learning based latent space embedding including using
attention weights, linear algebra based embeddings such as SVD, clustering,
hashing, to name a few. The community has come up with a useful set of metrics
to identify correctly the prediction for head or tail labels.
