Robust sparse component analysis based on a generalized hough transform

Eurasip Journal on Advances in Signal Processing

Volumn 2007, Issue , 2007, Pages

  Theis, Fabian J ^a   Georgiev, Pando ^b   Cichocki, Andrzej ^c,d  

^a UNIVERSITY OF REGENSBURG   (Germany)

^b University of Cincinnati   (United States)

^c RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

^d WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; GLOBAL OPTIMIZATION; MULTIVARIABLE SYSTEMS; NUMERICAL METHODS; ROBUSTNESS (CONTROL SYSTEMS); TENSORS;

COST FUNCTIONS; HOUGH ACCUMULATOR TENSOR; ROBUST SPARSE COMPONENT ANALYSIS;

HOUGH TRANSFORMS;

EID: 33846239022     PISSN: 11108657     EISSN: 16870433     Source Type: Journal    
DOI: 10.1155/2007/52105     Document Type: Article

References (30)

1. D. H. Ballard Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 2 1981 111 122
2. P. Ballester J. Barnes D. R. Crabtree R. J. Hanisch Applications of the Hough transform. Astronomical Data Analysis Software and Systems III ASP Conference Series 61 1994
3. S. Barbarossa Analysis of multicomponent LFM signals by a combined Wigner-Hough transform. IEEE Transactions on Signal Processing 43 6 1995 1511 1515
4. P. S. Bradley bradley@microsoft.com O. L. Mangasarian k-plane clustering. Journal of Global Optimization 16 1 2000 23 32
5. S. S. Chen schen@watson.ibm.com D. L. Donoho donoho@stat.stanford.edu M. A. Saunders mike@SOL-Michael.stanford.edu Atomic decomposition by basis pursuit. SIAM Journal of Scientific Computing 20 1 1998 33 61
6. A. Cichocki S. Amari Adaptive Blind Signal and Image Processing. John Wiley & Sons New York, NY, USA 2002
7. L. Cirillo A. Zoubir M. Amin Direction finding of nonstationary signals using a time-frequency Hough transform. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05) Philadelphia, Pa, USA 2005 2718 2721
8. P. Comon Independent component analysis. A new concept? Signal Processing 36 3 1994 287 314
9. D. L. Donoho donoho@stat.stanford.edu M. Elad Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization. Proceedings of the National Academy of Sciences of the United States of America 100 5 2003 2197 2202
10. R. O. Duda P. E. Hart Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM 15 1972 1 204 208
11. R. Dudley Department of Mathematics, MIT, course 18.465, 2005
12. J. Eriksson V. Koivunen Identifiability and separability of linear ica models revisited. Proceedings of the 4th International Symposium on Independent Component Analysis and Blind Source Separation (ICA '03) Nara, Japan 2003 23 27
13. P. Georgiev P. Pardalos F. J. Theis A. Cichocki H. Bakardjian Sparse component analysis: a new tool for data mining. Data Mining in Biomedicine Springer New York, NY, USA 2005 in print
14. P. Georgiev F. J. Theis A. Cichocki P. Pardalos Optimization algorithms for sparse representations and applications. Multiscale Optimization Methods Springer New York, NY, USA 2005
15. P. Georgiev pgeorgie@ececs.uc.edu F. J. Theis fabian@theis.name A. Cichocki cia@bsp.brain.riken.jp Sparse component analysis and blind source separation of underdetermined mixtures. IEEE Transactions on Neural Networks 16 4 2005 992 996
16. A. Goldenshluger goldensh@stat.haifa.ac.il A. Zeevi assaf@gsb.columbia. edu The Hough transform estimator. Annals of Statistics 32 5 2004 1908 1932
17. P. V. C. Hough Machine analysis of bubble chamber pictures. International Conference on High Energy Accelerators and Instrumentation CERN Geneva, Switzerland 554 556 1959
18. A. Hyvärinen J. Karhunen E. Oja Independent Component Analysis. John Wiley & Sons New York, NY, USA 2001
19. A. Hyvärinen E. Oja A fast fixed-point algorithm for independent component analysis. Neural Computation 9 7 1997 1483 1492
20. T.-W. Lee tewon@salk.edu M. S. Lewicki lewicki@salk.edu M. Girolami giro0ci@paisley.ac.uk T. J. Sejnowski Blind source separation of more sources than mixtures using overcomplete representations. IEEE Signal Processing Letters 6 4 1999 87 90
21. J. K. Lin D. G. Grier J. D. Cowan Feature extraction approach to blind source separation. Proceedings of the IEEE Workshop on Neural Networks for Signal Processing (NNSP '97) 1997 Amelia Island, Fla, USA 398 405
22. P. J. Rousseeuw A. M. Leroy Robust Regression and Outlier Detection. John Wiley & Sons New York, NY, USA 1987
23. H. Shindo Y. Hirai An approach to overcomplete-blind source separation using geometric structure. Proceedings of Annual Conference of Japanese Neural Network Society (JNNS '01) Nara, Japan Naramachi Center 2001 95 96
24. F. J. Theis fabian@theis.name A new concept for separability problems in blind source separation. Neural Computation 16 9 2004 1827 1850
25. F. J. Theis fabian@theis.name G. A. García garciaga@ieee.org On the use of sparse signal decomposition in the analysis of multi-channel surface electromyograms. Signal Processing 86 3 2006 603 623
26. F. J. Theis P. Georgiev A. Cichocki Robust overcomplete matrix recovery for sparse sources using a generalized Hough transform. Proceedings of 12th European Symposium on Artificial Neural Networks (ESANN '04) Bruges, Belgium 2004 343 348 d-side, Evere, Belgium
27. F. J. Theis fabian.theis@mathematik.uni-regensburg.de E. W. Lang C. G. Puntonet A geometric algorithm for overcomplete linear ICA. Neurocomputing 56 1-4 2004 381 398
28. F. J. Theis fabian@theis.name C. G. Puntonet E. W. Lang Median-based clustering for underdetermined blind signal processing. IEEE Signal Processing Letters 13 2 2006 96 99
29. K. Waheed F. Salem Algebraic overcomplete independent component analysis. Proceedings of the 4th International Symposium on Independent Component Analysis and Blind Source Separation (ICA '03) Nara, Japan 2003 1077 1082
30. M. Zibulevsky B. A. Pearlmutter Blind source separation by sparse decomposition in a signal dictionary. Neural Computation 13 4 2001 863 882