The Experts below are selected from a list of 8196 Experts worldwide ranked by ideXlab platform
Xian-da Zhang - One of the best experts on this subject based on the ideXlab platform.
-
A Generalized Contrast Function and Stability Analysis for Overdetermined Blind Separation of Instantaneous Mixtures
Neural Computation, 2006Co-Authors: Xiao-long Zhu, Xian-da ZhangAbstract:In this letter, the problem of blind separation of n independent sources from their m linear instantaneous mixtures is considered. First, a generalized contrast function is defined as a valuable extension of the existing classical and nonsymmetrical contrast functions. It is applicable to the overdetermined blind separation (m > n) with an unknown number of sources, because not only independent components but also redundant ones are allowed in the outputs of a separation system. Second, a natural gradient learning algorithm developed primarily for the complete case (m = n) is shown to work as well with an n × m or m × m Separating Matrix, for each optimizes a certain mutual information contrast function. Finally, we present stability analysis for a newly proposed generalized orthogonal natural gradient algorithm (which can perform the overdetermined blind separation when n is unknown), obtaining an expectable result that its local stability conditions are slightly stricter than those of the conventional natural gradient algorithm using an invertible mixing Matrix (m = n).
-
ISNN (1) - An novel algorithm for blind source separation with unknown sources number
Advances in Neural Networks - ISNN 2006, 2006Co-Authors: Shun-tian Lou, Hai-hong Jin, Xian-da ZhangAbstract:The natural gradient blind source separation (BSS) algorithm with unknown source number proposed by Cichocki in 1999 is justified in this paper. An new method to detect the redundant separated signals based on structure of Separating Matrix is proposed, by embedding it into the natural gradient algorithm, an novel BSS algorithm with an unknown source number is developed. The novel algorithm can successfully separate source signals and converge stably, while the Cichocki’s algorithm would diverge inevitably. The new method embedded in novel algorithm can detect and cancel the redundant separated signals within 320 iteration, which is far quicker than the method based on the decorrelation, if some parameters are chosen properly.
-
An Novel Algorithm for Blind Source Separation with Unknown Sources Number
Lecture Notes in Computer Science, 2006Co-Authors: Shun-tian Lou, Hai-hong Jin, Xian-da ZhangAbstract:The natural gradient blind source separation (BSS) algorithm with unknown source number proposed by Cichocki in 1999 is justified in this paper. An new method to detect the redundant separated signals based on structure of Separating Matrix is proposed, by embedding it into the natural gradient algorithm, an novel BSS algorithm with an unknown source number is developed. The novel algorithm can successfully separate source signals and converge stably, while the Cichocki's algorithm would diverge inevitably. The new method embedded in novel algorithm can detect and cancel the redundant separated signals within 320 iteration, which is far quicker than the method based on the decorrelation. if some parameters are chosen properly.
-
Adaptive RLS algorithm for blind source separation using a natural gradient
IEEE Signal Processing Letters, 2002Co-Authors: Xiao-long Zhu, Xian-da ZhangAbstract:By using the natural gradient on the Stiefel manifold to minimize a nonlinear principle component analysis criterion, this letter proposes a new adaptive recursive-least-squares (RLS) algorithm with prewhitening for blind source separation (BSS), which makes full use of the orthogonality constraint of the Separating Matrix. Simulations show that the new natural-gradient-based RLS algorithm has faster convergence than the existing least-mean-square algorithms and RLS algorithm for BSS.
Kazunori Yamaguchi - One of the best experts on this subject based on the ideXlab platform.
-
joint approximate diagonalization utilizing aic based decision in the jacobi method
International Conference on Artificial Neural Networks, 2009Co-Authors: Yoshitatsu Matsuda, Kazunori YamaguchiAbstract:Joint approximate diagonalization is one of well-known methods for solving independent component analysis and blind source separation. It calculates an orthonormal Separating Matrix which diagonalizes many cumulant matrices of given observed signals as accurately as possible. It has been known that such diagonalization can be carried out efficiently by the Jacobi method, where the optimization for each pair of signals is repeated until the convergence of the whole Separating Matrix. Generally, the Jacobi method decides whether the optimization is actually applied to a given pair by a convergence decision condition. Then, the whole convergence is achieved when no pair is actually optimized any more. Though this decision condition is crucial for accelerating the speed of the whole optimization, many previous works have employed simple conditions based on an arbitrarily selected threshold. In this paper, we propose a novel decision condition which is based on Akaike information criterion (AIC). It is derived by assuming each cumulant Matrix to be a sample generated independently. In each pair optimization, the condition compares the reduction rate of the objective function with a constant depending on the number of cumulant matrices. It involves no thresholds (and no parameters) to be set manually. Numerical experiments verify that the proposed decision condition can accelerate the optimization speed for artificial data.
-
ICANN (2) - Joint Approximate Diagonalization Utilizing AIC-Based Decision in the Jacobi Method
Artificial Neural Networks – ICANN 2009, 2009Co-Authors: Yoshitatsu Matsuda, Kazunori YamaguchiAbstract:Joint approximate diagonalization is one of well-known methods for solving independent component analysis and blind source separation. It calculates an orthonormal Separating Matrix which diagonalizes many cumulant matrices of given observed signals as accurately as possible. It has been known that such diagonalization can be carried out efficiently by the Jacobi method, where the optimization for each pair of signals is repeated until the convergence of the whole Separating Matrix. Generally, the Jacobi method decides whether the optimization is actually applied to a given pair by a convergence decision condition. Then, the whole convergence is achieved when no pair is actually optimized any more. Though this decision condition is crucial for accelerating the speed of the whole optimization, many previous works have employed simple conditions based on an arbitrarily selected threshold. In this paper, we propose a novel decision condition which is based on Akaike information criterion (AIC). It is derived by assuming each cumulant Matrix to be a sample generated independently. In each pair optimization, the condition compares the reduction rate of the objective function with a constant depending on the number of cumulant matrices. It involves no thresholds (and no parameters) to be set manually. Numerical experiments verify that the proposed decision condition can accelerate the optimization speed for artificial data.
-
ICONIP (1) - An Adaptive Threshold in Joint Approximate Diagonalization by the Information Criterion
Neural Information Processing, 2009Co-Authors: Yoshitatsu Matsuda, Kazunori YamaguchiAbstract:Joint approximate diagonalization is one of well-known methods for solving independent component analysis and blind source separation. It calculates an orthonormal Separating Matrix which diagonalizes many cumulant matrices of given observed signals as accurately as possible. It has been known that such diagonalization can be carried out efficiently by the Jacobi method, where the optimization for each pair of signals is repeated until the convergence of the whole Separating Matrix. The Jacobi method decides whether the optimization is actually applied to a given pair by a convergence decision condition. Generally, a fixed threshold is used as the condition. Though a sufficiently small threshold is desirable for the accuracy of results, the speed of convergence is quite slow if the threshold is too small. In this paper, we propose a new decision condition with an adaptive threshold for joint approximate diagonalization. The condition is theoretically derived by a model selection approach to a simple generative model of cumulants in the similar way as in Akaike information criterion. In consequence, the adaptive threshold is given as the current average of all the cumulants. Only if the expected reduction of the cumulants on each pair is larger than the adaptive threshold, the pair is actually optimized. Numerical results verify that the method can choose a suitable threshold for artificial data and image separation.
Shun-tian Lou - One of the best experts on this subject based on the ideXlab platform.
-
Robust nonlinear power iteration algorithm for adaptive blind separation of independent signals
Digital Signal Processing, 2010Co-Authors: Wei-tao Zhang, Shun-tian Lou, Yan-liang ZhangAbstract:Abstract In this paper, we elaborate an extension of classical power iteration method to nonlinear power iteration for blind separation of multiple independent sources from observed array output signals. The present algorithm, referred to as NPI, considers the estimating of the Separating Matrix as a nonlinear power iteration problem. By naturally choosing the positive definite normalizer for nonlinear power term, the resulting algorithm not only yields robust convergence behavior but also guarantees the orthonormality of the Separating Matrix at each iteration. To circumvent the difficulty of solving the inverse square root for the normalizer, an efficient adaptive singular value decomposition (SVD) technique is also adopted to obtain a fast implementation of NPI. The estimation accuracy and convergence speed of the present algorithm are illustrated through simulation results and compared with the existing adaptive algorithms.
-
ISNN (1) - An novel algorithm for blind source separation with unknown sources number
Advances in Neural Networks - ISNN 2006, 2006Co-Authors: Shun-tian Lou, Hai-hong Jin, Xian-da ZhangAbstract:The natural gradient blind source separation (BSS) algorithm with unknown source number proposed by Cichocki in 1999 is justified in this paper. An new method to detect the redundant separated signals based on structure of Separating Matrix is proposed, by embedding it into the natural gradient algorithm, an novel BSS algorithm with an unknown source number is developed. The novel algorithm can successfully separate source signals and converge stably, while the Cichocki’s algorithm would diverge inevitably. The new method embedded in novel algorithm can detect and cancel the redundant separated signals within 320 iteration, which is far quicker than the method based on the decorrelation, if some parameters are chosen properly.
-
An Novel Algorithm for Blind Source Separation with Unknown Sources Number
Lecture Notes in Computer Science, 2006Co-Authors: Shun-tian Lou, Hai-hong Jin, Xian-da ZhangAbstract:The natural gradient blind source separation (BSS) algorithm with unknown source number proposed by Cichocki in 1999 is justified in this paper. An new method to detect the redundant separated signals based on structure of Separating Matrix is proposed, by embedding it into the natural gradient algorithm, an novel BSS algorithm with an unknown source number is developed. The novel algorithm can successfully separate source signals and converge stably, while the Cichocki's algorithm would diverge inevitably. The new method embedded in novel algorithm can detect and cancel the redundant separated signals within 320 iteration, which is far quicker than the method based on the decorrelation. if some parameters are chosen properly.
Andrzej Cichocki - One of the best experts on this subject based on the ideXlab platform.
-
Blind Source Separation Algorithms with Matrix Constraints
IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2003Co-Authors: Andrzej Cichocki, Pando GeorgievAbstract:In many applications of Independent Component Analysis (ICA) and Blind Source Separation (BSS) estimated sources signals and the mixing or Separating matrices have some special structure or some constraints are imposed for the matrices such as symmetries, orthogonality, non-negativity, sparseness and specified invariant norm of the Separating Matrix. In this paper we present several algorithms and overview some known transformations which allows us to preserve several important constraints. Computer simulation experiments confirmed validity and usefulness of the developed algorithms. key words: Blind sources separation, independent component analysis with constraints, non-negative blind source separation
-
INVITED PAPER Special Section on Special Issue on Blind Signal Processing Blind Source Separation Algorithms with Matrix Constraints
2003Co-Authors: Andrzej Cichocki, Pando GeorgievAbstract:SUMMARY In many applications of Independent Component Analysis (ICA) and Blind Source Separation (BSS) estimated sources signals and the mixing or Separating matrices have some special structure or some constraints are imposed for the matrices such as symmetries, orthogonality, non-negativity, sparseness and specified invariant norm of the Separating Matrix. In this paper we present several algorithms and overview some known transformations which allows us to preserve several important constraints. Computer simulation experiments confirmed validity and usefulness of the developed algorithms.
-
Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation
Neural computation, 2000Co-Authors: Shun-ichi Amari, Tianping Chen, Andrzej CichockiAbstract:Independent component analysis or blind source separation extracts independent signals from their linear mixtures without assuming prior knowledge of their mixing coefficients. It is known that the independent signals in the observed mixtures can be successfully extracted except for their order and scales. In order to resolve the indeterminacy of scales, most learning algorithms impose some constraints on the magnitudes of the recovered signals. However, when the source signals are nonstationary and their average magnitudes change rapidly, the constraints force a rapid change in the magnitude of the Separating Matrix. This is the case with most applications (e.g., speech sounds, electroencephalogram signals). It is known that this causes numerical instability in some cases. In order to resolve this difficulty, this article introduces new nonholonomic constraints in the learning algorithm. This is motivated by the geometrical consideration that the directions of change in the Separating Matrix should be orthogonal to the equivalence class of Separating matrices due to the scaling indeterminacy. These constraints are proved to be nonholonomic, so that the proposed algorithm is able to adapt to rapid or intermittent changes in the magnitudes of the source signals. The proposed algorithm works well even when the number of the sources is overestimated, whereas the existent algorithms do not (assuming the sensor noise is negligibly small), because they amplify the null components not included in the sources. Computer simulations confirm this desirable property.
Xiao-long Zhu - One of the best experts on this subject based on the ideXlab platform.
-
A Generalized Contrast Function and Stability Analysis for Overdetermined Blind Separation of Instantaneous Mixtures
Neural Computation, 2006Co-Authors: Xiao-long Zhu, Xian-da ZhangAbstract:In this letter, the problem of blind separation of n independent sources from their m linear instantaneous mixtures is considered. First, a generalized contrast function is defined as a valuable extension of the existing classical and nonsymmetrical contrast functions. It is applicable to the overdetermined blind separation (m > n) with an unknown number of sources, because not only independent components but also redundant ones are allowed in the outputs of a separation system. Second, a natural gradient learning algorithm developed primarily for the complete case (m = n) is shown to work as well with an n × m or m × m Separating Matrix, for each optimizes a certain mutual information contrast function. Finally, we present stability analysis for a newly proposed generalized orthogonal natural gradient algorithm (which can perform the overdetermined blind separation when n is unknown), obtaining an expectable result that its local stability conditions are slightly stricter than those of the conventional natural gradient algorithm using an invertible mixing Matrix (m = n).
-
Adaptive RLS algorithm for blind source separation using a natural gradient
IEEE Signal Processing Letters, 2002Co-Authors: Xiao-long Zhu, Xian-da ZhangAbstract:By using the natural gradient on the Stiefel manifold to minimize a nonlinear principle component analysis criterion, this letter proposes a new adaptive recursive-least-squares (RLS) algorithm with prewhitening for blind source separation (BSS), which makes full use of the orthogonality constraint of the Separating Matrix. Simulations show that the new natural-gradient-based RLS algorithm has faster convergence than the existing least-mean-square algorithms and RLS algorithm for BSS.
