Large-scale Multi-label Learning with Missing Labels

@inproceedings{Yu2014LargescaleML,
  title={Large-scale Multi-label Learning with Missing Labels},
  author={Hsiang-Fu Yu and Prateek Jain and Inderjit S. Dhillon},
  booktitle={ICML},
  year={2014}
}
The multi-label classification problem has generated significant interest in recent years. However, existing approaches do not adequately address two key challenges: (a) the ability to tackle problems with a large number (say millions) of labels, and (b) the ability to handle data with missing labels. In this paper, we directly address both these problems by studying the multi-label problem in a generic empirical risk minimization (ERM) framework. Our framework, despite being simple, is… CONTINUE READING
Highly Influential
This paper has highly influenced 39 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 207 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 1 time over the past 90 days. VIEW TWEETS

Citations

Publications citing this paper.
Showing 1-10 of 142 extracted citations

208 Citations

020406020142015201620172018
Citations per Year
Semantic Scholar estimates that this publication has 208 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 22 references

Introduction to the non-asymptotic analysis of random matrices, chapter 5 of Compressed Sensing, Theory and Applications, pp. 210–268

  • Vershynin, Roman
  • 2012

Multi-label classification with principal label space transformation

  • Tai, Farbound, Lin, Hsuan-Tien
  • Neural Computation,
  • 2012

Similar Papers

Loading similar papers…