A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf
.
Sparse Local Embeddings for Extreme Multi-label Classification
2015
Neural Information Processing Systems
The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches attempt to make training and prediction tractable by assuming that the training label matrix is low-rank and reducing the effective number of labels by projecting the high dimensional label vectors onto a low dimensional linear subspace. Still, leading embedding approaches have
dblp:conf/nips/BhatiaJKVJ15
fatcat:y42jjydpzzhcjkkrmqysys7pf4