| tensorBSS-package {tensorBSS} | R Documentation |
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 <joni.virta@outlook.com>
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.