Accelerated Gradient Method for Multi-task Sparse Learning Problem

@article{Chen2009AcceleratedGM,
  title={Accelerated Gradient Method for Multi-task Sparse Learning Problem},
  author={Xi Chen and Weike Pan and James T. Kwok and Jaime G. Carbonell},
  journal={2009 Ninth IEEE International Conference on Data Mining},
  year={2009},
  pages={746-751}
}
Many real world learning problems can be recast as multi-task learning problems which utilize correlations among different tasks to obtain better generalization performance than learning each task individually. The feature selection problem in multi-task setting has many applications in fields of computer vision, text classification and bio-informatics. Generally, it can be realized by solving a L-1-infinity regularized optimization problem. And the solution automatically yields the joint… CONTINUE READING
Highly Influential
This paper has highly influenced 15 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 352 citations. REVIEW CITATIONS
107 Extracted Citations
23 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 107 extracted citations

352 Citations

050100'11'13'15'17
Citations per Year
Semantic Scholar estimates that this publication has 352 citations based on the available data.

See our FAQ for additional information.

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 23 references

Online and batch learning using forward looking subgradients

  • J. Duchi, Y. Singer
  • 2008.
  • 2008
Highly Influential
4 Excerpts

Introductory lectures on convex optimization: a basic course

  • Y. Nesterov
  • Kluwer Academic Pub,
  • 2003
Highly Influential
6 Excerpts

Similar Papers

Loading similar papers…