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Learning Overcomplete Representations

by Michael S. Lewicki, Terrence J. Sejnowski , 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 ..."
Abstract - Cited by 354 (10 self) - Add to MetaCart
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

by Jean-François Cardoso - 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 higher-order moments, to identify source signatures in array data without ..."
Abstract - Cited by 126 (7 self) - Add to MetaCart
any a-priori 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 Time-Varying Mixtures using Second-Order Statistics

by Tzahi Weisman, Arie Yeredor
"... 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 pe-riodic processes like breathing, heart-beating etc. Assuming stationary sources with distinct spectra, we rely on second-order 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

by Scott C. Douglas, Andrzej Cichocki, Shun-ichi Amari - 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 recently-developed algorithms have shown promise in these tasks, these techniques may fail to separate signal mixtures containing both su ..."
Abstract - Cited by 37 (15 self) - Add to MetaCart
using a rigorously-derived 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 arbitrary-distributed

Blind Source Separation for . . .

by Herbert Buchner, Robert Aichner, Walter Kellermann , 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 second-order statistics (SOS), higher-order 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 second-order statistics (SOS), higher-order statistics (HOS) must

Sequential blind source separation based exclusively on second order statistics developed for a class of periodic signals

by Maria G. Jafari, Wenwu Wang, Jonathon A. Chambers, Tetsuya Hoya, Andrzej Cichocki, Senior Member, Senior Member - 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 second-order statistical information and exploits the assumption that the source signals have distinct periods. Separation is performed by sequen ..."
Abstract - Cited by 5 (2 self) - Add to MetaCart
is also shown to mitigate the limitation that the EASI algorithm can separate at most one Gaussian distributed source. Furthermore, the steady-state performance of the proposed algorithm is compared with that of EASI and the block-based second-order blind identification (SOBI) method. Index Terms—Blind

Joint blind source separation with multivariate Gaussian model: algorithms and performance analysis

by Matthew Anderson, Student Member, Tülay Adalı, Xi-lin Li - 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 ..."
Abstract - Cited by 11 (1 self) - Add to MetaCart
vector analysis, joint blind source separation, permutation problem, second-order statistics. I.

An adaptive subspace algorithm for blind separation of independent sources in convolutive mixture

by Ali Mansour, Christian Jutten - 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 second-order statistics (but more sensors than sources). Furthermore, the s ..."
Abstract - Cited by 12 (4 self) - Add to MetaCart
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 second-order statistics (but more sensors than sources). Furthermore

Blind source separation based on the fractional Fourier transform

by Sharon Karako-eilon, Arie Yeredor, David Mendlovic - 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 widely-used algorithms are based on second-order statistics, and some of these algorithms are based on time-frequency ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
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 widely-used algorithms are based on second-order statistics, and some of these algorithms are based on time

Coil sensitivity encoding for fast MRI. In:

by Klaas P Pruessmann , Markus Weiger , Markus B Scheidegger , Peter Boesiger - 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 ..."
Abstract - Cited by 193 (3 self) - Add to MetaCart
and technical concerns, inherent limits of k-space speed have almost been reached. An entirely different approach to sub-wavelength 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 of 932