%0 Conference Paper %1 HuGySze15 %A Huang, R. %A György, A. %A Szepesvári, Cs. %B ICML %D 2015 %K analysis complexity, component independent learning sample theory, %P 2521--2530 %T Deterministic Independent Component Analysis %X We study independent component analysis with noisy observations. We present, for the first time in the literature, consistent, polynomial-time algorithms to recover non-Gaussian source signals and the mixing matrix with a reconstruction error that vanishes at a rate of T^1/2 using T observations and scales only polynomially with the natural parameters of the problem. Our algorithms and analysis also extend to deterministic source signals whose empirical distributions are approximately independent.

@inproceedings{HuGySze15, abstract = {We study independent component analysis with noisy observations. We present, for the first time in the literature, consistent, polynomial-time algorithms to recover non-Gaussian source signals and the mixing matrix with a reconstruction error that vanishes at a rate of T^{1/2} using T observations and scales only polynomially with the natural parameters of the problem. Our algorithms and analysis also extend to deterministic source signals whose empirical distributions are approximately independent. }, added-at = {2020-03-17T03:03:01.000+0100}, author = {Huang, R. and Gy{\"o}rgy, A. and Szepesv{\'a}ri, {Cs}.}, biburl = {https://www.bibsonomy.org/bibtex/2e68fcdb51d00ccc2d484c68b4c6cb2dd/csaba}, booktitle = {ICML}, date-added = {2015-04-25 18:24:02 +0000}, date-modified = {2015-08-02 00:44:57 +0000}, interhash = {c7643defc251e88a29ca0c29617c935f}, intrahash = {e68fcdb51d00ccc2d484c68b4c6cb2dd}, keywords = {analysis complexity, component independent learning sample theory,}, pages = {2521--2530}, pdf = {papers/ICML15-DICA.pdf}, timestamp = {2020-03-17T03:03:01.000+0100}, title = {Deterministic Independent Component Analysis}, year = 2015 }