tensorBSS-package: Blind Source Separation Methods for Tensor-Valued...

In tensorBSS: Blind Source Separation Methods for Tensor-Valued Observations

Description

Contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: ‘tPCA’, ‘tFOBI’, ‘tJADE’, ‘k-tJADE’, ‘tgFOBI’, ‘tgJADE’, ‘tSOBI’, ‘tNSS.SD’, ‘tNSS.JD’, ‘tNSS.TD.JD’, ‘tPP’ and ‘tTUCKER’.

Details

Package:	tensorBSS
Type:	Package
Version:	0.3.8
Date:	2021-06-02
License:	GPL (>= 2)

Author(s)

Joni Virta, Christoph Koesner, Bing Li, Klaus Nordhausen, Hannu Oja and Una Radojicic

Maintainer: Joni Virta <[e-mail]>

References

Virta, J., Taskinen, S. and Nordhausen, K. (2016), Applying fully tensorial ICA to fMRI data, Signal Processing in Medicine and Biology Symposium (SPMB), 2016 IEEE, doi: 10.1109/SPMB.2016.7846858

Virta, J., Li, B., Nordhausen, K. and Oja, H., (2017), Independent component analysis for tensor-valued data, Journal of Multivariate Analysis, doi: 10.1016/j.jmva.2017.09.008

Virta, J. and Nordhausen, K., (2017), Blind source separation of tensor-valued time series. Signal Processing 141, 204-216, doi: 10.1016/j.sigpro.2017.06.008

Virta J., Nordhausen K. (2017): Blind source separation for nonstationary tensor-valued time series, 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), doi: 10.1109/MLSP.2017.8168122

Virta J., Li B., Nordhausen K., Oja H. (2018): JADE for tensor-valued observations, Journal of Computational and Graphical Statistics, 27, 628 - 637, doi: 10.1080/10618600.2017.1407324

Virta J., Lietzen N., Ilmonen P., Nordhausen K. (2021): Fast tensorial JADE, Scandinavian Journal of Statistics, 48, 164-187, doi: 10.1111/sjos.12445

Koesner, C, Nordhausen, K. and Virta, J. (2019), Estimating the signal tensor dimension using tensorial PCA. Manuscript.

tensorBSS documentation built on June 2, 2021, 9:08 a.m.