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932
Learning Overcomplete Representations
, 2000
"... In an overcomplete basis, the number of basis vectors is greater than the dimensionality of the input, and the representation of an input is not a unique combination of basis vectors. Overcomplete representations have been advocated because they have greater robustness in the presence of noise, can ..."
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Cited by 354 (10 self)
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basis (or dictionary). We present an algorithm for learning an overcomplete basis by viewing it as probabilistic model of the observed data. We show that overcomplete bases can yield a better approximation of the underlying statistical distribution of the data and can thus lead to greater coding
Source Separation Using Higher Order Moments
 in Proc. ICASSP
, 1989
"... This communication presents a simple algebraic method for the extraction of independent components in multidimensional data. Since statistical independence is a much stronger property than uncorrelation, it is possible, using higherorder moments, to identify source signatures in array data without ..."
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Cited by 126 (7 self)
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any apriori model for propagation or reception, that is, without directional vector parametrization, provided that the emitting sources be independent with different probability distributions. We propose such a "blind" identification procedure. Source signatures are directly identified
Separation of Periodically TimeVarying Mixtures using SecondOrder Statistics
"... Abstract. We address the problem of Blind Source Separation (BSS) in the context of instantaneous (memoryless) linear mixtures, where the unknown mixing coefficients are time varying, changing periodically in time. Such a mixing model is realistic, e.g., when considering a biological or physiologica ..."
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or physiological system where the mixing coefficients are affected by periodic processes like breathing, heartbeating etc. Assuming stationary sources with distinct spectra, we rely on secondorder statistics (SOS) and offer an expansion of the classical Second Order Blind Identification (SOBI) algorithm
Multichannel blind separation and deconvolution of sources with arbitrary distributions
 In Proc. IEEE Workshop on Neural Networks for Signal Processing, pages 436445, Almelia Island
, 1997
"... Abstract { Blind deconvolution and separation of linearly mixed and convolved sources is an important andchallenging task for numerous applications. While several recentlydeveloped algorithms have shown promise in these tasks, these techniques may fail to separate signal mixtures containing both su ..."
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Cited by 37 (15 self)
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using a rigorouslyderived su cient criterion for stability and then selects the appropriate nonlinearityforeachchannel suchthat local convergence conditions of the algorithm are satis ed. Extensive simulations show thevalidity and e ciency of our method to blindly extract mixtures of arbitrarydistributed
Blind Source Separation for . . .
, 2004
"... Blind source separation (BSS) algorithms for time series can exploit three properties of the source signals: nonwhiteness, nonstationarity, and nongaussianity. While methods utilizing the first two properties are usually based on secondorder statistics (SOS), higherorder statistics (HOS) must be ..."
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Blind source separation (BSS) algorithms for time series can exploit three properties of the source signals: nonwhiteness, nonstationarity, and nongaussianity. While methods utilizing the first two properties are usually based on secondorder statistics (SOS), higherorder statistics (HOS) must
Sequential blind source separation based exclusively on second order statistics developed for a class of periodic signals
 IEEE Trans. On Signal Processing
"... Abstract—A sequential algorithm for the blind separation of a class of periodic source signals is introduced in this paper. The algorithm is based only on secondorder statistical information and exploits the assumption that the source signals have distinct periods. Separation is performed by sequen ..."
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Cited by 5 (2 self)
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is also shown to mitigate the limitation that the EASI algorithm can separate at most one Gaussian distributed source. Furthermore, the steadystate performance of the proposed algorithm is compared with that of EASI and the blockbased secondorder blind identification (SOBI) method. Index Terms—Blind
Joint blind source separation with multivariate Gaussian model: algorithms and performance analysis
 IEEE Trans. Signal Process
, 2012
"... Abstract—In this paper, we consider the joint blind source separation (JBSS) problem and introduce a number of algorithms to solve the JBSS problem using the independent vector analysis (IVA) framework. Source separation of multiple datasets simultaneously is possible when the sources within each an ..."
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Cited by 11 (1 self)
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vector analysis, joint blind source separation, permutation problem, secondorder statistics. I.
An adaptive subspace algorithm for blind separation of independent sources in convolutive mixture
 IEEE Trans. on Signal Processing
, 2000
"... We propose an algorithm for blind separation of sources in convolutive mixtures based on a subspace approach. The advantage of this algorithm is that it reduces a convolutive mixture to an instantaneous mixture by using only secondorder statistics (but more sensors than sources). Furthermore, the s ..."
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Cited by 12 (4 self)
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We propose an algorithm for blind separation of sources in convolutive mixtures based on a subspace approach. The advantage of this algorithm is that it reduces a convolutive mixture to an instantaneous mixture by using only secondorder statistics (but more sensors than sources). Furthermore
Blind source separation based on the fractional Fourier transform
 In Proc. 4th Int. Symp. on Independent Component Analysis, ICA2003, Japan
, 2003
"... Different approaches have been suggested in recent years to the blind source separation problem, in which a set of signals is recovered out of its instantaneous linear mixture. Many widelyused algorithms are based on secondorder statistics, and some of these algorithms are based on timefrequency ..."
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Cited by 3 (0 self)
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Different approaches have been suggested in recent years to the blind source separation problem, in which a set of signals is recovered out of its instantaneous linear mixture. Many widelyused algorithms are based on secondorder statistics, and some of these algorithms are based on time
Coil sensitivity encoding for fast MRI. In:
 Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
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Cited by 193 (3 self)
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and technical concerns, inherent limits of kspace speed have almost been reached. An entirely different approach to subwavelength resolution in MRI is based on the fact that with a receiver placed near the object the contribution of a signal source to the induced voltage varies appreciably with its relative
Results 1  10
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932