Sparse Infomax Based on Hoyer Projection and its Application to Simulated Structural MRI and SNP Data

Kuaikuai Duan, Rogers F. Silva, Jiayu Chen, Dongdong Lin, Vince D. Calhoun, Jingyu Liu

Research output: Contribution to journalArticlepeer-review


Independent component analysis has been widely applied to brain imaging and genetic data analyses for its ability to identify interpretable latent sources. Nevertheless, leveraging source sparsity in a more granular way may further improve its ability to optimize the solution for certain data types. For this purpose, we propose a sparse infomax algorithm based on nonlinear Hoyer projection, leveraging both sparsity and statistical independence of latent sources. The proposed algorithm iteratively updates the unmixing matrix by infomax (for independence) and the sources by Hoyer projection (for sparsity), feeding the sparse sources back as input data for the next iteration. Consequently, sparseness propagates effectively through infomax iterations, producing sources with more desirable properties. Simulation results on both brain imaging and genetic data demonstrate that the proposed algorithm yields improved pattern recovery, particularly under low signal-to-noise ratio conditions, as well as improved sparseness compared to traditional infomax.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Mar 7 2019
Externally publishedYes


  • Hoyer projection
  • Imaging data
  • Pattern recovery
  • SNP data
  • Sparse infomax

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Immunology and Microbiology(all)
  • Neuroscience(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)


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