Bi-level multi-source learning for heterogeneous block-wise missing data

@article{Xiang2014BilevelML,
  title={Bi-level multi-source learning for heterogeneous block-wise missing data},
  author={Shuo Xiang and Lei Yuan and Wei Fan and Yalin Wang and Paul M. Thompson and Jieping Ye},
  journal={NeuroImage},
  year={2014},
  volume={102},
  pages={192-206}
}
Bio-imaging technologies allow scientists to collect large amounts of high-dimensional data from multiple heterogeneous sources for many biomedical applications. In the study of Alzheimer's Disease (AD), neuroimaging data, gene/protein expression data, etc., are often analyzed together to improve predictive power. Joint learning from multiple complementary data sources is advantageous, but feature-pruning and data source selection are critical to learn interpretable models from high-dimensional… CONTINUE READING
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 20 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 44 references

Optimization with sparsity-inducing penalties

  • F. Bach
  • Foundations and Trendstextregistered in Machine…
  • 2011
Highly Influential
4 Excerpts

A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data

  • V. D. Calhoun, J. Liu, T. Adalı
  • NeuroImage
  • 2009
Highly Influential
4 Excerpts

Bi-level multi-source learning fo dx.doi.org/10.1016/j.neuroimage.2013.08.015

  • S Xiang
  • 2013
1 Excerpt

Similar Papers

Loading similar papers…