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Fast score function estimation with application in ICA

International Conference on Artificial Neural Networks (ICANN 2001)

作者： Vlassis, N Univ Amsterdam Inst Comp Sci NL-1098 SJ Amsterdam Netherlands

ISBN: (纸本)3540424865

We describe an efficient method for accurate estimation of the score function of a random variable, which can be regarded as an extension of the FFT-based fast density estimation method of Silverman (1982), and which scales no more than linearly with the sample size. We demonstrate the utility of our approach in a real-life ICA problem involving the separation of eight sound signals, where better results are observed than using state-of-the-art ICA methods.

关键词： score function estimation independent component analysis blind signal separation

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A Non-Linear Blind Source Separation Method Based on Perceptron Structure and Conjugate Gradient Algorithm

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2014年第11期33卷 3573-3590页

作者： Li, Wei Yang, Huizhong Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Jiangsu Peoples R China

The linear mixing model has been considered previously in most of the researches which are devoted to the blind source separation (BSS) problem. In practice, a more realistic BSS mixing model should be the non-linear one. In this paper, we propose a non-linear BSS method, in which a two-layer perceptron network is employed as the separating system to separate sources from observed non-linear mixture signals. The learning rules for the parameters of the separating system are derived based on the minimum mutual information criterion with conjugate gradient algorithm. Instead of choosing a proper non-linear functions empirically, the adaptive kernel density estimation is used in order to estimate the probability density functions and their derivatives of the separated signals. As a result, the score function of the perceptron's outputs can be estimated directly. Simulations show good performance of the proposed non-linear BSS algorithm.

关键词： Non-linear mixtures Blind signal separation score function estimation Conjugate gradient algorithm

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Efficient source adaptivity in independent component analysis

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2001年第3期12卷 559-566页

作者： Vlassis, N Motomura, Y Univ Amsterdam RWCP Autonomous Learning Funct SNN Dept Comp Sci NL-1098 SJ Amsterdam Netherlands Electrotech Lab Tsukuba Ibaraki 3058568 Japan

A basic element in most independent component analysis (ICA) algorithms is the choice of a model for the score functions of the unknown sources. While this is usually based on approximations, for large data sets it is possible to achieve "source adaptivity" by directly estimating from the data the "'true" score functions of the sources. In this paper we describe an efficient scheme for achieving this by extending the fast density estimation method of Silverman (1982), We show with a real and a synthetic experiment that our method can provide more accurate solutions than state-of-the-art methods when optimization is carried out in the vicinity of the global minimum of the contrast function.

关键词： blind signal separation independent component analysis (ICA) score function estimation source adaptivity

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Fast algorithms for mutual information based independent component analysis

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2004年第10期52卷 2690-2700页

作者： Pham, DT CNRS IMAG Lab Modeling & Computat Grenoble 9 France

This paper provides fast algorithms to perform independent component analysis based on the mutual information criterion. The main ingredient is the binning technique and the use of cardinal splines, which allows the fast computation of the density estimator over a regular grid. Using a discretized form of the entropy, the criterion can be evaluated quickly together with its gradient, which can be expressed in terms of the score functions. Both offline and online separation algorithms have been developed. Our density, entropy, and score estimators also have their own interest.

关键词： binning blind source separation independence component analysis cardinal spline entropy estimation kernel density estimation mutual information score function estimation

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Blind Separation for Sub-/Super-Gaussian Sources with Momentum Term Based on Entropy Maximization

Blind Separation for Sub-/Super-Gaussian Sources with Moment...

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第三十二届中国控制会议

作者： LI Wei YANG Huizhong Key Laboratory of Advanced Process Control for Light Industry of Jiangnan University Shanghai Key Laboratory of Power station Automation Technology

ISBN: (纸本)9781479900305

Blind source separation consists in processing a set of observed mixed signals to separate them into a set of original *** this paper,an adaptive blind source separation method based on the entropy maximization criterion is proposed. Momentum term is added into the updating rules to speed up the algorithm and improve the convergence ***,an adaptive estimation of the score function for both sub-Gaussian and super-Gaussian signals is *** results show that the proposed method can separate signals with different kurtosis.

关键词： Blind Source Separation Entropy Maximization Adaptive Learning score function estimation

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