IVA-package: Tools for Independent Vector Analysis
In ivaBSS: Tools for Independent Vector Analysis
ivaBSS-package R Documentation
Tools for Independent Vector Analysis
Description
Independent vector analysis (IVA) is a blind source separation (BSS) model where several datasets are jointly unmixed. This package provides several methods for the unmixing together with some performance measures. For details, see Anderson et al. (2011) <doi:10.1109/TSP.2011.2181836> and Lee et al. (2007) <doi:10.1016/j.sigpro.2007.01.010>.
Details
The package contains tools for independent vector analysis. The main functions to perform IVA are "IVANewton" and "fastIVA". "NewtonIVA" performs Newton update based IVA and "fastIVA" performs fixed-point iteration based IVA. Both of the algorithms have multiple options for source density models.
Author(s)
Authors: Mika Sipilä, Klaus Nordhausen, Sara Taskinen
Maintainer: Mika Sipilä
References
Anderson, M., Adalı, T., & Li, X.-L. (2011). Joint blind source separation with multivariate
Gaussian model: Algorithms and performance analysis. IEEE Transactions on Signal
Processing, 60, 1672–1683. <doi:10.1109/TSP.2011.2181836>
Anderson, M. (2013). Independent vector analysis: Theory, algorithms, and applications.
PhD dissertation, University of Maryland, Baltimore County.
ivaBSS documentation built on May 19, 2022, 5:09 p.m.
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| ivaBSS-package | R Documentation |
Tools for Independent Vector Analysis
Description
Independent vector analysis (IVA) is a blind source separation (BSS) model where several datasets are jointly unmixed. This package provides several methods for the unmixing together with some performance measures. For details, see Anderson et al. (2011) <doi:10.1109/TSP.2011.2181836> and Lee et al. (2007) <doi:10.1016/j.sigpro.2007.01.010>.
Details
The package contains tools for independent vector analysis. The main functions to perform IVA are "IVANewton" and "fastIVA". "NewtonIVA" performs Newton update based IVA and "fastIVA" performs fixed-point iteration based IVA. Both of the algorithms have multiple options for source density models.
Author(s)
Authors: Mika Sipilä, Klaus Nordhausen, Sara Taskinen
Maintainer: Mika Sipilä
References
Anderson, M., Adalı, T., & Li, X.-L. (2011). Joint blind source separation with multivariate Gaussian model: Algorithms and performance analysis. IEEE Transactions on Signal Processing, 60, 1672–1683. <doi:10.1109/TSP.2011.2181836>
Anderson, M. (2013). Independent vector analysis: Theory, algorithms, and applications. PhD dissertation, University of Maryland, Baltimore County.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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