Citation Relationships
| Legends: | Link to a Model | Reference cited by multiple papers |
Zhu XL, Zhang XD, Ye JM (2006) A Generalized Contrast Function and Stability Analysis for Overdetermined Blind Separation of Instantaneous Mixtures Neural Comput 18:709-728
References and models cited by this paper | References and models that cite this paper | |
Amari S (1998) Natural gradient works efficiently in learning Neural Comput 10:251-276 Amari S, Chen TP, Cichocki A (2000) Nonholonomic orthogonal learning algorithms for blind source separation. Neural Comput 12:1463-84 [PubMed] Amari S, Cichocki A (1998) Adaptive blind signal processing: Neural network approaches Proc IEEE 86:2026-2048 Amari SL, Cichocki A, Yang HH (1996) A new learning algorithm for blind signal separation. Advances in Neural Information Processing Systems., Touretzky D:Mozer M:Hasselmo M, ed. pp.757 Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129-59 [PubMed] Cao XR, Liu RW (1996) General approach to blind source separation IEEE Transactions On Signal Processing 44:562-571 Cardoso JF (1998) Blind source separation: Statistical principles Proc IEEE 86:2009-2025 Cardoso JF (1999) High-order contrasts for independent component analysis. Neural Comput 11:157-92 [PubMed] Cardoso JF (2000) On the stability of source separation algorithms Journal Of VLSI Signal Processing 26:7-14 Cardoso JF, Laheld B (1996) Equivalent adaptive source separation. IEEE Trans Signal Proc 44:3017-3030 Cichocki A, Chen T, Amari S (1997) Stability Analysis of Learning Algorithms for Blind Source Separation. Neural Netw 10:1345-1351 [PubMed] Cichocki A, Karhunen J, Kasprzak W, Vigario R (1999) Neural networks for blind separation with unknown number of sources Neurocomputing 24:55-93 Cichocki A, Sabala I, Choi S, Orsier B, Szupiluk R (1997) Self-adaptive independent component analysis for sub-gaussian and super-gaussian mixtures with an unknown number of sources and additive noise International Symposium On Nonlinear Theory And Its Applications 2:731-734 Cichocki A, Unbehauen R, Rummert E (1994) Robust learning algorithm for blind separation of signals Electronics Letters 30:1386-1387 Comon P (1994) Independent component analysis, a new concept? Signal Processing 36:287-314 Cover TM, Thomas JA (1991) Elements of Information Theory Delfosse N, Loubaton P (1995) Adaptive blind separation of independent sources: A deflation approach Signal Processing 45:59-83 Douglas SC (2002) Simple algorithms for decorrelation-based blind source separation IEEE Workshop On Neural Networks For Signal Processing 12:545-554 Girolami M (1999) Self-organising neural networks: Independent component analysis and blind source separation Golub GH, van_Loan CF (1996) Matrix computations Hyvarinen A, Karunen J, Oja E (2001) Independent component analysis Hyvarinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis Neural Comput 9:1483-1492 Karhunen J, Pajunen P, Oja E (1998) The nonlinear PCA criterion in blind source separation: Relations with other approaches Neurocomputing 22:5-20 Lewicki MS, Sejnowski TJ (2000) Learning overcomplete representations. Neural Comput 12:337-65 [PubMed] Li YQ, Wang J (2002) Sequential blind extraction of instantaneously mixed sources IEEE Trans On Signal Processing 50:997-1006 Mathis H, Douglas SC (2002) On the existence of universal nonlinearities for blind source separation IEEE Trans Signal Processing 50:1007-1016 Moreau E, Macchi O (1996) High-order contrasts for self-adaptive source separation International Journal Of Adaptive Control And Signal Processi 10:19-46 Moreau E, Thirion-Moreau N (1999) Nonsymmetrical contrasts for source separation IEEE Trans On Signal Processing 47:2241-2252 Ohata M, Matsuoka K (2002) Stability analysis of information-theoretic blind separation algorithms in the case where the sources are nonlinear processes IEEE Trans Signal Processing 50:69-77 Oja E (1997) The nonlinear PCA learning rule and signal separation: Mathematical analysis Neurocomputing 17:25-46 Pham DT (2002) Mutual information approach to blind separation of stationary sources IEEE Trans Information Theory 48:1935-1946 Pham DT, Cardoso JF (2001) Blind separation of instantaneous mixtures of nonstationary sources IEEE Trans On Signal Processing 49:1837-1848 Sherman DA, Pasion SG, Forsburg SL (1998) Multiple domains of fission yeast Cdc19p (MCM2) are required for its association with the core MCM complex. Mol Biol Cell 9:1833-45 [PubMed] Tong L, Liu R, Soon VC, Huang YF (1991) Indeterminacy and identifiability of blind identification IEEE Trans On Circuits And Systems 38:499-509 von_Hoff TP, Lindgren AG, Kaelin AN (2000) Transpose properties in the stability and performance of the classical adaptive algorithms for blind source separation and deconvolution Signal Processing 80:1807-1822 Yang HH, Amari S (1997) Adaptive on-line learning algorithms for blind separation: Maximum entropy and minimum mutual information Neural Comput 9:457-482 Ye JM, Zhu XL, Zhang XD (2004) Adaptive Blind Separation with an Unknown Number of Sources Neural Comput 16:1641-1660 Zhang LQ, Cichocki A, Amari S (1999) Natural gradient algorithm for blind separation of over-determined mixtures with additive noise IEEE Signal Processing Letters 6:293-295 Zhu XL, Zhang XD (2002) Adaptive RLS algorithm for blind source separation using a natural gradient Ieee Signal Processing Letters 9:432-435 Zhu XL, Zhang XD (2004) Overdetermined blind source separation based on singular value decomposition J Elect Inf Tech 26:337-343 | ||