Blind signal separation in the presence of Gaussian noise

Journal of Machine Learning Research

Volumn 30, Issue , 2013, Pages 270-287

  Belkin, Mikhail ^a   Rademacher, Luis ^a   Voss, James ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BLIND SOURCE SEPARATION; INDEPENDENT COMPONENT ANALYSIS; MICROPHONES; RANDOM VARIABLES; RECOVERY; SIGNAL PROCESSING;

ADDITIVE GAUSSIAN NOISE; BLIND SIGNAL SEPARATION; COCKTAIL PARTY PROBLEMS; INDEPENDENT COMPONENT ANALYSIS(ICA); INDEPENDENT COMPONENTS; INDEPENDENT RANDOM VARIABLES; MACHINE LEARNING LITERATURE; UNDERLYING DISTRIBUTION;

GAUSSIAN NOISE (ELECTRONIC);

EID: 84898029889     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (20)

1. Sanjeev Arora, Rong Ge, Ankur Moitra, and Sushant Sachdeva. Provable ICA with unknown Gaussian noise, and implications for Gaussian mixtures and autoencoders. Neural Information Processing Systems, 2012. to appear.
2. A.J. Bell and T.J. Sejnowski. The "independent components" of natural scenes are edge filters. Vision research, 37(23): 3327-3338, 1997.
3. J.F. Cardoso and A. Souloumiac. Blind beamforming for non-Gaussian signals. In Radar and Signal Processing, IEE Proceedings F, Volume 140, pages 362-370. IET, 1993.
4. Pierre Comon and Christian Jutten, editors. Handbook of Blind Source Separation. Academic Press, 2010.
5. N. Delfosse and P. Loubaton. Adaptive blind separation of independent sources: a deflation approach. Signal processing, 45(1): 59-83, 1995.
6. Alan M. Frieze, Mark Jerrum, and Ravi Kannan. Learning linear transformations. In FOCS, pages 359-368, 1996.
7. Daniel Hsu and Sham M. Kakade. Learning mixtures of spherical Gaussians: moment methods and spectral decompositions, 2012.
8. A. Hyvarinen. Gaussian moments for noisy independent component analysis. Signal Processing Letters, IEEE, 6(6): 145-147, 1999.
9. Aapo Hyvärinen. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10(3): 626-634, 1999.
10. Aapo Hyvärinen and Erkki Oja. Independent component analysis: algorithms and applications. Neural Networks, 13(4-5): 411-430, 2000.
11. T.P. Jung, S. Makeig, C. Humphries, T.W. Lee, M.J. Mckeown, V. Iragui, and T.J. Sejnowski. Removing electroencephalographic artifacts by blind source separation. Psychophysiology, 37(2): 163-178, 2000.
12. Maurice Kendall, Alan Stuart, and J. Keith Ord. Kendall's advanced theory of statistics. Vol. 1. Halsted Press, sixth edition, 1994. Distribution theory.
13. S. Makino, T.W. Lee, and H. Sawada. Blind speech separation. Springer, 2007.
14. Peter McCullagh. Tensor methods in statistics. Monographs on Statistics and Applied Probability. Chapman & Hall, London, 1987. ISBN 0-412-27480-9.
15. J. Morton and L.-H. Lim. Principal cumulant component analysis. Preprint.
16. Phong Q. Nguyen and Oded Regev. Learning a parallelepiped: Cryptanalysis of GGH and NTRU signatures. J. Cryptology, 22(2): 139-160, 2009.
17. Amnon Shashua and Tamir Hazan. Non-negative tensor factorization with applications to statistics and computer vision. In ICML, pages 792-799, 2005.
18. G. W. Stewart and Ji Guang Sun. Matrix perturbation theory. Computer Science and Scientific Computing. Academic Press Inc., Boston, MA, 1990. ISBN 0-12-670230-6.
19. Santosh S. Vempala and Ying Xiao. Structure from local optima: Learning subspace juntas via higher order pca. CoRR, abs/1108.3329, 2011.
20. Arie Yeredor. Blind source separation via the second characteristic function. Signal Processing, 80(5): 897-902, 2000.