Online blind source separation using incremental nonnegative matrix factorization with volume constraint

IEEE Transactions on Neural Networks

Volumn 22, Issue 4, 2011, Pages 550-560

  Zhou, Guoxu ^a   Yang, Zuyuan ^a   Xie, Shengli ^a   Yang, Jun Mei ^a  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Blind source separation; dependent sources; incremental learning; nonnegative matrix factorization

Indexed keywords

COMPUTATIONAL COST PROBLEMS; COMPUTATIONAL COSTS; DEPENDENT SOURCES; DUAL-ENERGY X-RAY; ENCRYPTED SPEECH; FACE IMAGES; IDENTIFIABILITY; INCREMENTAL LEARNING; MIXING MATRIX; NATURAL GRADIENT; NONNEGATIVE MATRIX FACTORIZATION; NONUNIQUENESS; VOLUME CONSTRAINT;

FACTORIZATION; MATRIX ALGEBRA;

BLIND SOURCE SEPARATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED TOMOGRAPHY; FACE; HUMAN; METHODOLOGY; MONTE CARLO METHOD; ONLINE SYSTEM;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FACE; HUMANS; MONTE CARLO METHOD; ONLINE SYSTEMS; PATTERN RECOGNITION, AUTOMATED; TOMOGRAPHY, X-RAY COMPUTED;

EID: 79953821926     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2011.2109396     Document Type: Article

References (42)

1. A. Hyvarinen, "Fast and robust fixed-point algorithms for independent component analysis," IEEE Trans. Neural Netw., vol. 10, no. 3, pp. 626-634, May 1999.
2. A. Cichocki and R. Unbehauen, "Robust neural networks with on-line learning for blind identification and blind separation of sources," IEEE Trans. Circuits Syst. I: Fundam. Theory Appl., vol. 43, no. 11, pp. 894-906, Nov. 1996. (Pubitemid 126777534)
3. Y. Xiang, S. K. Ng, and V. Nguyen, "Blind separation of mutually correlated sources using precoders," IEEE Trans. Neural Netw., vol. 21, no. 1, pp. 82-90, Jan. 2010.
4. A. Cichocki and S. Amari, Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. New York: Wiley, 2003.
5. J.-F. Cardoso and B. H. Laheld, "Equivariant adaptive source separation," IEEE Trans. Signal Process., vol. 44, no. 12, pp. 3017-3030, Dec. 1996. (Pubitemid 126778048)
6. C. Jutten and J. Herault, "Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture," Signal Process., vol. 24, no. 1, pp. 1-10, Jul. 1991. (Pubitemid 21679270)
7. P. Comon, "Independent component analysis, a new concept?" Signal Process., vol. 36, no. 3, pp. 287-314, Apr. 1994.
8. F. Yin, T. Mei, and J. Wang, "Blind-source separation based on decorrelation and nonstationarity," IEEE Trans. Circuits Syst. I, vol. 54, no. 5, pp. 1150-1158, May 2007. (Pubitemid 46773784)
9. S. Li, C. Li, K.-T. Lo, and G. Chen, "Cryptanalyzing an encryption scheme based on blind source separation," IEEE Trans. Circuits Syst. I, vol. 55, no. 4, pp. 1055-1063, May 2008. (Pubitemid 351802620)
10. S. L. Xie, Z. Y. Yang, and F. L. Fu, "Nonnegative matrix factorization applied to nonlinear speech and image cryptosystems," IEEE Trans. Circuits Syst. I, vol. 55, no. 8, pp. 2356-2367, Sep. 2008.
11. D. Liu, S. Hu, and H. Zhang, "Simultaneous blind separation of instantaneous mixtures with arbitrary rank," IEEE Trans. Circuits Syst. I, vol. 53, no. 10, pp. 2287-2298, Oct. 2006. (Pubitemid 44637795)
12. Q. Lin, F. Yin, T. Mei, and H. Liang, "A blind source separation based method for speech encryption," IEEE Trans. Circuits Syst. I, vol. 53, no 6, pp. 1320-1328, Jun. 2006. (Pubitemid 44209545)
13. K. Stadlthanner, D. Lutter, F. J. Theis, E. W. Lang, A. M. Tome, P. Georgieva, and C. G. Puntonet, "Sparse nonnegative matrix factorization with genetic algorithms for microarray analysis," in Proc. Int. Joint onf. Neural Netw., Orlando, FL, Aug. 2007, pp. 294-299.
14. H. Kim and H. P. Denning, "Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis," Bioinformatics, vol. 23, no. 12, pp. 1495-1502, May 2007. (Pubitemid 47244474)
15. L. Miao and H. Qi, "Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 3, pp. 765-777, Mar. 2007. (Pubitemid 46375748)
16. U. Manmontri and P. A. Naylor, "A class of frobenius norm-based algorithms using penalty term and natural gradient for blind signal separation," IEEE Trans. Audio, Speech, Lang. Process., vol. 16, no. 6, pp. 1181-1193, Aug. 2008.
17. X.-L. Zhu, X.-D. Zhang, Z.-Z. Ding, and Y. Jia, "Adaptive nonlinear PCA algorithms for blind source separation without prewhitening," IEEE Trans. Circuits Syst. I, vol. 53, no. 3, pp. 745-753, Mar. 2006.
18. W. X. He, J. Y. Xie, and Y. P. Yang, "On-line blind source separation algorithm based on second order statistics," J. Syst. Eng. Electron., vol. 16, no. 3, pp. 692-696, 2005.
19. C. F. Caiafa and A. N. Proto, "Separation of statistically dependent sources using an L2-distance non-Gaussianity measure," Signal Process., vol. 86, no. 11, pp. 3404-3420, Nov. 2006. (Pubitemid 44331427)
20. I. Kopriva and D. Seršić, "Wavelet packets approach to blind separation of statistically dependent sources," Neurocomputing, vol. 71, nos. 7-9, pp. 1642-1655, Mar. 2008. (Pubitemid 351373173)
21. D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization," Nature, vol. 401, pp. 788-791, Oct. 1999.
22. A. Cichocki, R. Zdunek, and S. Amari, "New algorithms for nonnegative matrix factorization in applications to blind source separation," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., vol. 5. Toulouse, France, May 2006, pp. 621-624.
23. A. Cichocki, A. H. Phan, R. Zdunek, and L. Q. Zhang, "Flexible component analysis for sparse, smooth, nonnegative coding or representation," in Proc. 14th Int. Conf. Neural Process., Kitakyushu, Japan, 2008, pp. 811-820.
24. M. D. Craig, "Minimum-volume transforms for remotely sensed data," IEEE Trans. Geosci. Remote Sens., vol. 32, no. 3, pp. 542-552, May 1994.
25. F.-Y. Wang, C.-Y. Chi, T.-H. Chan, and Y. Wang, "Nonnegative leastcorrelated component analysis for separation of dependent sources by volume maximization," IEEE Trans. Patttern Anal. Mach. Intell., vol. 32, no. 5, pp. 875-888, May 2010.
26. J. Y. Weng, Y. L. Zhang, and W.-S. Hwang, "Candid covariance-free incremental principal component analysis," IEEE Trans. Patttern Anal. Mach. Intell., vol. 25, no. 8, pp. 1034-1040, Aug. 2003.
27. Y. Q. Li, A. Cichocki, and S. Amari, "Analysis of sparse representation and blind source separation," Neural Comput., vol. 16, no. 6, pp. 1193-1234, Jun. 2004. (Pubitemid 38640622)
28. S.-G. Kim and C. D. Yoo, "Underdetermined blind source separation based on subspace representation," IEEE Trans. Signal Process., vol. 57, no. 7, pp. 2604-2614, Jul. 2009.
29. D. D. Lee and H. S. Seung, "Algorithms for nonnegative matrix factorization," in Proc. Adv. Neural Inf. Process. Syst., vol. 13. 2001, pp. 556-562.
30. R. Zdunek and A. Cichocki, "Nonnegative matrix factorization with constrained second-order optimization," Signal Process., vol. 87, no. 8, pp. 1904-1916, Aug. 2007. (Pubitemid 46590394)
31. S. S. Bucak and B. Gunsel, "Incremental subspace learning via nonnegative matrix factorization," Pattern Recognit., vol. 42, no. 5, pp. 788-797, May 2009.
32. Y. Li, "On incremental and robust subspace learning," Pattern Recognit., vol. 37, no. 7, pp. 1509-1518, Jul. 2004. (Pubitemid 38653378)
33. M. W. Berry, M. Browne, A. N. Langville, V. P. Pauca, and R. J. Plemmons, "Algorithms and applications for approximate nonnegative matrix factorization," Comput. Stat. & Data Anal., vol. 52, no. 1, pp. 155-173, Sep. 2007. (Pubitemid 47331703)
34. S. Amari, "Natural gradient works efficiently in learning," Neural Comput., vol. 10, no. 2, pp. 251-276, Feb. 1998. (Pubitemid 128463152)
35. A. Cichocki, R. Zdunek, and S. Amari, "Nonnegative matrix and tensor factorization," IEEE Signal Process. Mag., vol. 26, no. 1, pp. 142-145, Jan. 2008.
36. J. Eggert and E. Körner, "Sparse coding and NMF," in Proc. IEEE Int. Conf. Neural. Netw., vol. 4. Jul. 2004, pp. 2529-2533. (Pubitemid 40011434)
37. R. Albright, J. Cox, D. Duling, A. N. Langville, and C. D. Meyer, "Algorithms, initializations, and convergence for the nonnegative matrix factorization," in Proc. 12th ACM SIGKDD Int. Conf. Knowl. Disc. & Data Mining, 2006, pp. 1-18.
38. C.-J. Lin, "On the convergence of multiplicative update algorithms for nonnegative matrix factorization," IEEE Trans. Neural Netw., vol. 18, no. 6, pp. 1589-1596, Nov. 2007. (Pubitemid 350148414)
39. L. Grippo and M. Sciandrone, "On the convergence of the block nonlinear Gauss-Seidel method under convex constraints," Oper. Res. Lett., vol. 26, no. 3, pp. 127-136, Apr. 2000. (Pubitemid 30564332)
40. V. Monga and M. K. Mihcak, "Robust and secure image hashing via non-negative matrix factorization," IEEE Trans. Inf. Foren. Secur., vol. 2, no. 3, pp. 376-390, Sep. 2007. (Pubitemid 47290573)
41. K. Suzuki, R. Engelmann, H. MacMahon, and K. Doi. (2006).Virtual dual-energy radiography: Improved chest radiographs by means of rib suppression based on a massive training artificial neural network (MTANN). Radiology [Online]. Available: http://suzukilab.uchicago.edu/research.htm
42. A. Cichocki, S. Amari, K. Siwek, T. Tanaka, and A. H. Phan. (2007). ICALAB Toolboxes [Online]. Available: http://www.bsp.brain.riken.jp/ICALAB