A novel deterministic method for large-scale blind source separation. Boussé, M., Debals, O., & De Lathauwer, L. In *2015 23rd European Signal Processing Conference (EUSIPCO)*, pages 1890-1894, Aug, 2015.

Paper doi abstract bibtex

Paper doi abstract bibtex

A novel deterministic method for blind source separation is presented. In contrast to common methods such as independent component analysis, only mild assumptions are imposed on the sources. On the contrary, the method exploits a hypothesized (approximate) intrinsic low-rank structure of the mixing vectors. This is a very natural assumption for problems with many sensors. As such, the blind source separation problem can be reformulated as the computation of a tensor decomposition by applying a low-rank approximation to the tensorized mixing vectors. This allows the introduction of blind source separation in certain big data applications, where other methods fall short.

@InProceedings{7362712, author = {M. Boussé and O. Debals and L. {De Lathauwer}}, booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)}, title = {A novel deterministic method for large-scale blind source separation}, year = {2015}, pages = {1890-1894}, abstract = {A novel deterministic method for blind source separation is presented. In contrast to common methods such as independent component analysis, only mild assumptions are imposed on the sources. On the contrary, the method exploits a hypothesized (approximate) intrinsic low-rank structure of the mixing vectors. This is a very natural assumption for problems with many sensors. As such, the blind source separation problem can be reformulated as the computation of a tensor decomposition by applying a low-rank approximation to the tensorized mixing vectors. This allows the introduction of blind source separation in certain big data applications, where other methods fall short.}, keywords = {approximation theory;blind source separation;decomposition;deterministic algorithms;independent component analysis;tensors;vectors;deterministic method;large-scale blind source separation;independent component analysis;hypothesized intrinsic low-rank structure;mixing vector;sensor;low-rank approximation;tensor decomposition;Tensile stress;Approximation methods;Blind source separation;Sensors;Europe;Big data;Blind source separation;big data;higher-order tensor;tensor decomposition;low-rank approximation}, doi = {10.1109/EUSIPCO.2015.7362712}, issn = {2076-1465}, month = {Aug}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570085947.pdf}, }

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