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

Description Details Author(s) References


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’ and ‘tPP’.


Package: tensorBSS
Type: Package
Version: 0.3.4
Date: 2018-03-01
License: GPL (>= 2)


Joni Virta, Bing Li, Klaus Nordhausen and Hannu Oja

Maintainer: Joni Virta <[email protected]>


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., Li, B., Nordhausen, K. and Oja, H., (2017), JADE for Tensor-Valued Observation, to appear in Journal of Computational and Graphical Statistics. preprint available on ArXiv http://arxiv.org/abs/1603.05406.

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

tensorBSS documentation built on March 18, 2018, 2:12 p.m.