Results 1  10
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311
Independent Component Analysis
 Neural Computing Surveys
, 2001
"... A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the ..."
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Cited by 1492 (93 self)
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A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the original data. Wellknown linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes the statistical dependence of the components of the representation. Such a representation seems to capture the essential structure of the data in many applications. In this paper, we survey the existing theory and methods for ICA. 1
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed SubGaussian and SuperGaussian Sources
, 1999
"... An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able to blindly separate mixed signals with sub and superGaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a pro ..."
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Cited by 202 (21 self)
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An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able to blindly separate mixed signals with sub and superGaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have suband superGaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub and superGaussian regimes. We demonstrate that the extended infomax algorithm is able to easily separate 20 sources with a variety of source distributions. Applied to highdimensional data from electroencephalographic (EEG) recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical ...
HighOrder Contrasts for Independent Component Analysis
"... This article considers highorder measures of independence for the independent component analysis problem and discusses the class of Jacobi algorithms for their optimization. Several implementations are discussed. We compare the proposed approaches with gradientbased techniques from the algorithmic ..."
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Cited by 187 (4 self)
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This article considers highorder measures of independence for the independent component analysis problem and discusses the class of Jacobi algorithms for their optimization. Several implementations are discussed. We compare the proposed approaches with gradientbased techniques from the algorithmic point of view and also on a set of biomedical data.
Embracing wireless interference: Analog network coding
 in ACM SIGCOMM
, 2007
"... Traditionally, interference is considered harmful. Wireless networks strive to avoid scheduling multiple transmissions at the same time in order to prevent interference. This paper adopts the opposite approach; it encourages strategically picked senders to interfere. Instead of forwarding packets, r ..."
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Cited by 148 (9 self)
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Traditionally, interference is considered harmful. Wireless networks strive to avoid scheduling multiple transmissions at the same time in order to prevent interference. This paper adopts the opposite approach; it encourages strategically picked senders to interfere. Instead of forwarding packets, routers forward the interfering signals. The destination leverages networklevel information to cancel the interference and recover the signal destined to it. The result is analog network coding because it mixes signals not bits. So, what if wireless routers forward signals instead of packets? Theoretically, such an approach doubles the capacity of the canonical relay network. Surprisingly, it is also practical. We implement our design using software radios and show that it achieves significantly higher throughput than both traditional wireless routing and prior work on wireless network coding. 1.
Blind Separation of Instantaneous Mixtures of Non Stationary Sources
 IEEE Trans. Signal Processing
, 2000
"... Most ICA algorithms are based on a model of stationary sources. This paper considers exploiting the (possible) nonstationarity of the sources to achieve separation. We introduce two objective functions based on the likelihood and on mutual information in a simple Gaussian non stationary model and w ..."
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Cited by 126 (11 self)
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Most ICA algorithms are based on a model of stationary sources. This paper considers exploiting the (possible) nonstationarity of the sources to achieve separation. We introduce two objective functions based on the likelihood and on mutual information in a simple Gaussian non stationary model and we show how they can be optimized, offline or online, by simple yet remarkably efficient algorithms (one is based on a novel joint diagonalization procedure, the other on a Newtonlike technique). The paper also includes (limited) numerical experiments and a discussion contrasting nonGaussian and nonstationary models. 1. INTRODUCTION The aim of this paper is to develop a blind source separation procedure adapted to source signals with time varying intensity (such as speech signals). For simplicity, we shall restrict ourselves to the simplest mixture model: X(t) = AS(t) (1) where X(t) = [X 1 (t) XK (t)] T is the vector of observations (at time t), A is a fixed unknown K K inver...
Blind source separation of more sources than mixtures using overcomplete representations
 IEEE Sig. Proc. Lett
, 1999
"... Abstract—Empirical results were obtained for the blind source separation of more sources than mixtures using a recently proposed framework for learning overcomplete representations. This technique assumes a linear mixing model with additive noise and involves two steps: 1) learning an overcomplete r ..."
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Cited by 100 (2 self)
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Abstract—Empirical results were obtained for the blind source separation of more sources than mixtures using a recently proposed framework for learning overcomplete representations. This technique assumes a linear mixing model with additive noise and involves two steps: 1) learning an overcomplete representation for the observed data and 2) inferring sources given a sparse prior on the coefficients. We demonstrate that three speech signals can be separated with good fidelity given only two mixtures of the three signals. Similar results were obtained with mixtures of two speech signals and one music signal. Index Terms—Blind source separation, independent component analysis, overcomplete dictionary, overcomplete representation, speech signal separation. (a) (b)
Measuring statistical dependence with HilbertSchmidt norms
 PROCEEDINGS ALGORITHMIC LEARNING THEORY
, 2005
"... We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the HilbertSchmidt norm of the crosscovariance operator (we term this a HilbertSchmidt Independence Criterion, or HSIC). Th ..."
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Cited by 95 (41 self)
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We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the HilbertSchmidt norm of the crosscovariance operator (we term this a HilbertSchmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernelbased independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no userdefined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernelbased criteria, and of other recently published ICA methods.
InformationTheoretic Analysis of Interscale and Intrascale Dependencies Between Image Wavelet Coefficients
 IEEE Transactions on Image Processing
, 2001
"... This paper presents an informationtheoretic analysis of statistical dependencies between image wavelet coefficients. The dependencies are measured using mutual information, which has a fundamental relationship to data compression, estimation, and classification performance. ..."
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Cited by 70 (1 self)
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This paper presents an informationtheoretic analysis of statistical dependencies between image wavelet coefficients. The dependencies are measured using mutual information, which has a fundamental relationship to data compression, estimation, and classification performance.
Independent component approach to the analysis of EEG and MEG recordings
 IEEE Transactions on Biomedical Engineering
, 2000
"... This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Helsinki University of Technology's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this m ..."
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Cited by 57 (8 self)
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This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Helsinki University of Technology's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to
WaveletBased Image Estimation: An Empirical Bayes Approach Using Jeffreys' Noninformative Prior
, 2001
"... The sparseness and decorrelation properties of the discrete wavelet transform have been exploited to develop powerful denoising methods. However, most of these methods have free parameters which have to be adjusted or estimated. In this paper, we propose a waveletbased denoising technique without a ..."
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Cited by 50 (11 self)
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The sparseness and decorrelation properties of the discrete wavelet transform have been exploited to develop powerful denoising methods. However, most of these methods have free parameters which have to be adjusted or estimated. In this paper, we propose a waveletbased denoising technique without any free parameters; it is, in this sense, a "universal" method. Our approach uses empirical Bayes estimation based on a Jeffreys' noninformative prior; it is a step toward objective Bayesian waveletbased denoising. The result is a remarkably simple fixed nonlinear shrinkage/thresholding rule which performs better than other more computationally demanding methods.