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21
An informationmaximization approach to blind separation and blind deconvolution
 NEURAL COMPUTATION
, 1995
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A Unifying Informationtheoretic Framework for Independent Component Analysis
, 1999
"... We show that different theories recently proposed for Independent Component Analysis (ICA) lead to the same iterative learning algorithm for blind separation of mixed independent sources. We review those theories and suggest that information theory can be used to unify several lines of research. Pea ..."
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Cited by 83 (8 self)
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We show that different theories recently proposed for Independent Component Analysis (ICA) lead to the same iterative learning algorithm for blind separation of mixed independent sources. We review those theories and suggest that information theory can be used to unify several lines of research. Pearlmutter and Parra (1996) and Cardoso (1997) showed that the infomax approach of Bell and Sejnowski (1995) and the maximum likelihood estimation approach are equivalent. We show that negentropy maximization also has equivalent properties and therefore all three approaches yield the same learning rule for a fixed nonlinearity. Girolami and Fyfe (1997a) have shown that the nonlinear Principal Component Analysis (PCA) algorithm of Karhunen and Joutsensalo (1994) and Oja (1997) can also be viewed from informationtheoretic principles since it minimizes the sum of squares of the fourthorder marginal cumulants and therefore approximately minimizes the mutual information (Comon, 1994). Lambert (19...
Multichannel Blind Deconvolution: Fir Matrix Algebra And Separation Of Multipath Mixtures
, 1996
"... A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and mat ..."
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Cited by 73 (0 self)
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A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and matrix algorithms for use in multichannel /multipath problems. Using abstract algebra/group theoretic concepts, information theoretic principles, and the Bussgang property, methods of single channel filtering and source separation of multipath mixtures are merged into a general FIR matrix framework. Techniques developed for equalization may be applied to source separation and vice versa. Potential applications of these results lie in neural networks with feedforward memory connections, wideband array processing, and in problems with a multiinput, multioutput network having channels between each source and sensor, such as source separation. Particular applications of FIR polynomial matrix alg...
Blind Source Separation Using Temporal Predictability
, 2001
"... A measure of temporal predictability is defined and used to separate linear mixtures of signals. Given any set of statistically independent source signals, it is conjectured here that a linear mixture of those signals has the following property: the temporal predictability of any signal mixture is l ..."
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Cited by 26 (1 self)
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A measure of temporal predictability is defined and used to separate linear mixtures of signals. Given any set of statistically independent source signals, it is conjectured here that a linear mixture of those signals has the following property: the temporal predictability of any signal mixture is less than (or equal to) that of any of its component source signals. It is shown that this property can be used to recover source signals from a set of linear mixtures of those signals by finding an unmixing matrix that maximizes a measure of temporal predictability for each recovered signal. This matrix is obtained as the solution to a generalized eigenvalue problem; such problems have scaling characteristics of O(N 3), where N is the number of signal mixtures. In contrast to independent component analysis, the temporal predictability method requires minimal assumptions regarding the probability density functions of source signals. It is demonstrated that the method can separate signal mixtures in which each mixture is a linear combination of source signals with supergaussian, subgaussian, and gaussian probability density functions and on mixtures of voices and music.
A Taxonomy for Spatiotemporal Connectionist Networks Revisited: The Unsupervised Case
 Neural Computation
, 2003
"... Spatiotemporal connectionist networks (STCN's) comprise an important class of neural models that can deal with patterns distributed both in time and space. In this paper, we widen the application domain of the taxonomy for supervised STCN's recently proposed by Kremer (2001) to the unsupervised case ..."
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Cited by 21 (1 self)
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Spatiotemporal connectionist networks (STCN's) comprise an important class of neural models that can deal with patterns distributed both in time and space. In this paper, we widen the application domain of the taxonomy for supervised STCN's recently proposed by Kremer (2001) to the unsupervised case. This is possible through a reinterpretation of the state vector as a vector of latent (hidden) variables, as proposed by Meinicke (2000). The goal of this generalized taxonomy is then to provide a nonlinear generative framework for describing unsupervised spatiotemporal networks, making it easier to compare and contrast their representational and operational characteristics. Computational properties, representational issues and learning are also discussed and a number of references to the relevant source publications are provided. It is argued that the proposed approach is simple and more powerful than the previous attempts, from a descriptive and predictive viewpoint. We also discuss the relation of this taxonomy with automata theory and state space modeling, and suggest directions for further work.
Blind separation and blind deconvolution: an informationtheoretic approach
 In Proc. ICASSP
, 1995
"... Blind separation and blind deconvolution are related problems in unsupervzsed learnzng. In blind separation [7], illustrated in Fig.la, aset ofsources, sl(t),..., sN(t), (different people speaking, music etc) are mixed together ..."
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Cited by 12 (0 self)
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Blind separation and blind deconvolution are related problems in unsupervzsed learnzng. In blind separation [7], illustrated in Fig.la, aset ofsources, sl(t),..., sN(t), (different people speaking, music etc) are mixed together
A Simple Threshold Nonlinearity For Blind Signal Separation
 in Proc. ISCAS
, 2000
"... A computationally simple nonlinearity in the form of a threshold device is shown to serve as contrast function in blind signal separation. Convergence is shown to be robust, fast, and comparable with that of more complex polynomial nonlinearities. Together with the known signum nonlinearity for supe ..."
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Cited by 8 (8 self)
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A computationally simple nonlinearity in the form of a threshold device is shown to serve as contrast function in blind signal separation. Convergence is shown to be robust, fast, and comparable with that of more complex polynomial nonlinearities. Together with the known signum nonlinearity for superGaussian distributions, which basically is a threshold device with the threshold set to zero, the general threshold nonlinearity (with an appropriate threshold) can separate any nonGaussian signals. 1. INTRODUCTION Blind signal separation using higherorder statistics either explicitly or implicitly has attracted many researchers whose main goal is to separate a set of mixed signals as fast as possible with the smallest residual mixing. Throughout this paper we assume a linear mixing and separation process as depicted in Fig. 1. A W s x u separation process mixing process sensors separated sources sources Figure 1: Blind source separation model. The measured signals x = [x 1 , . . . ...
SpaceTime Modems for Wireless Personal Communications
 IEEE Personal Communications
, 1998
"... This article reviews spacetime modem technology for mobile radio applications. We begin with motivations for the use of spacetime modems and then briefly discuss the challenges posed by wireless propagation. Next, we develop a signal model for the wireless environment. Channel estimation, equaliza ..."
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Cited by 7 (0 self)
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This article reviews spacetime modem technology for mobile radio applications. We begin with motivations for the use of spacetime modems and then briefly discuss the challenges posed by wireless propagation. Next, we develop a signal model for the wireless environment. Channel estimation, equalization, and filtering techniques for spacetime modems in the forward and reverse links are then discussed. Finally we review applications of spacetime modems to cellular systems and discuss industry trends.
Blind multiuser detection using linear prediction
 IEEE J. Sel. Areas Commun
, 1998
"... Abstract — We propose a blind multiuser detection technique for array processing and code division multiple access (CDMA) systems that does not require knowledge of the array geometry or transmitter signature sequences. The technique has two key elements: an adaptive algorithm for separating the sig ..."
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Cited by 7 (1 self)
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Abstract — We propose a blind multiuser detection technique for array processing and code division multiple access (CDMA) systems that does not require knowledge of the array geometry or transmitter signature sequences. The technique has two key elements: an adaptive algorithm for separating the signal subspace from the noise subspace and an adaptive whitener based on linear prediction. The proposed algorithm offers low complexity, fast convergence, compatibility with shaped signal constellations, nearWiener steadystate performance, and optimal near–far resistance. Index Terms — Adaptive subspace separation, array processing, blind source separation, cochannel demodulation, subspace tracking. I.
Gradient Adaptive Algorithms for ContrastBased Blind Deconvolution
"... { This paper presents extensions of stochastic gradient independent component analysis (ICA) methods to the blind deconvolution task. Of particular importance in these extensions are the constraints placed on the deconvolution system transfer function. While unitnorm constrained ICA approaches can ..."
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Cited by 6 (3 self)
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{ This paper presents extensions of stochastic gradient independent component analysis (ICA) methods to the blind deconvolution task. Of particular importance in these extensions are the constraints placed on the deconvolution system transfer function. While unitnorm constrained ICA approaches can be directly applied to the blind deconvolution task, an allpass lter constraint within the optimization procedure is more appropriate. We show how such constraints can be approximately imposed within gradient adaptive niteimpulseresponse (FIR) lter implementations by proper extensions of gradient techniques within the Stiefel manifold of orthonormal matrices. Both online timedomain and blockbased frequencydomain algorithms are described. Simulations verify the superior performance behaviors provided by our allpassconstrained algorithms over standard unitnormconstrained ICA algorithms in blind deconvolution tasks. accepted for publication in Journal of VLSI Signal Processing Sys...