Sparsity and independence: balancing two objectives in optimization for source separation with application to fMRI analysis

DOI10.1016/j.jfranklin.2017.07.003zbMath1395.94088OpenAlexW2735317774MaRDI QIDQ1661463

Yuri Levin-Schwartz, Zois Boukouvalas, Vince D. Calhoun, Tülay Adali

Publication date: 16 August 2018

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11603/19279

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Ridge regression; shrinkage estimators (Lasso) (62J07) Integer programming (90C10) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Stochastic approximation (62L20)

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Uses Software

• SimTB

Cites Work

• Unnamed Item
• Fast fixed-point independent vector analysis algorithms for convolutive blind source separation
• The auction algorithm: A distributed relaxation method for the assignment problem
• Blind Source Separation by Sparse Decomposition in a Signal Dictionary
• Decoding by Linear Programming
• Independent Component Analysis by Entropy Bound Minimization
• Comparing Measures of Sparsity
• Underdetermined blind source separation using sparse representations