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

  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},
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
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