The Experts below are selected from a list of 2469 Experts worldwide ranked by ideXlab platform
Bixio Rimoldi - One of the best experts on this subject based on the ideXlab platform.
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exact formula for the minimum Squared Euclidean Distance of cpfsk
IEEE Transactions on Communications, 1991Co-Authors: Bixio RimoldiAbstract:The exact expression for the minimum-Squared Euclidean Distance of continuous-phase frequency-shift keying (CPFSK) with modulation index h >
Ranjith Liyanapathirana - One of the best experts on this subject based on the ideXlab platform.
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on the exact formula for the minimum Squared Euclidean Distance of cpfsk
IEEE Transactions on Communications, 1994Co-Authors: N Ekanayake, Ranjith LiyanapathiranaAbstract:In a paper by Rimoldi (see ibid., vol.39, no.9, p.1280, 1991) the closed-form expression for the minimum Squared Euclidean Distance of continuous-phase frequency-shift-keyed (CPFSK) signals with modulation index h/spl les/1/2 was derived. The authors extend the result derived by Rimoldi to include any rational modulation index. >
W Firmanto - One of the best experts on this subject based on the ideXlab platform.
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performance and design of space time coding in fading channels
IEEE Transactions on Communications, 2003Co-Authors: Jinhong Yuan, Zhuo Chen, Branka Vucetic, W FirmantoAbstract:The pairwise-error probability upper bounds of space-time codes (STCs) in independent Rician fading channels are derived. Based on the performance analysis, novel code design criteria for slow and fast Rayleigh fading channels are developed. It is found that, in fading channels, the STC design criteria depend on the value of the possible diversity gain of the system. In slow fading channels, when the diversity gain is smaller than four, the code error performance is dominated by the minimum rank and the minimum determinant of the codeword Distance matrix. However, when the diversity gain is larger than, or equal to, four, the performance is dominated by the minimum Squared Euclidean Distance. Based on the proposed design criteria, new codes are designed and evaluated by simulation.
Jonathon A Chambers - One of the best experts on this subject based on the ideXlab platform.
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a multiplicative algorithm for convolutive non negative matrix factorization based on Squared Euclidean Distance
IEEE Transactions on Signal Processing, 2009Co-Authors: Wenwu Wang, Andrzej Cichocki, Jonathon A ChambersAbstract:Using the convolutive nonnegative matrix factorization (NMF) model due to Smaragdis, we develop a novel algorithm for matrix decomposition based on the Squared Euclidean Distance criterion. The algorithm features new formally derived learning rules and an efficient update for the reconstructed nonnegative matrix. Performance comparisons in terms of computational load and audio onset detection accuracy indicate the advantage of the Euclidean Distance criterion over the Kullback-Leibler divergence criterion.
Cedric Fevotte - One of the best experts on this subject based on the ideXlab platform.
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nonlinear hyperspectral unmixing with robust nonnegative matrix factorization
arXiv: Methodology, 2014Co-Authors: Cedric Fevotte, Nicolas DobigeonAbstract:This paper introduces a robust mixing model to describe hyperspectral data resulting from the mixture of several pure spectral signatures. This new model not only generalizes the commonly used linear mixing model, but also allows for possible nonlinear effects to be easily handled, relying on mild assumptions regarding these nonlinearities. The standard nonnegativity and sum-to-one constraints inherent to spectral unmixing are coupled with a group-sparse constraint imposed on the nonlinearity component. This results in a new form of robust nonnegative matrix factorization. The data fidelity term is expressed as a beta-divergence, a continuous family of dissimilarity measures that takes the Squared Euclidean Distance and the generalized Kullback-Leibler divergence as special cases. The penalized objective is minimized with a block-coordinate descent that involves majorization-minimization updates. Simulation results obtained on synthetic and real data show that the proposed strategy competes with state-of-the-art linear and nonlinear unmixing methods.
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automatic relevance determination in nonnegative matrix factorization with the beta divergence
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Cedric FevotteAbstract:This paper addresses the estimation of the latent dimensionality in nonnegative matrix factorization (NMF) with the β-divergence. The β-divergence is a family of cost functions that includes the Squared Euclidean Distance, Kullback-Leibler (KL) and Itakura-Saito (IS) divergences as special cases. Learning the model order is important as it is necessary to strike the right balance between data fidelity and overfitting. We propose a Bayesian model based on automatic relevance determination (ARD) in which the columns of the dictionary matrix and the rows of the activation matrix are tied together through a common scale parameter in their prior. A family of majorization-minimization (MM) algorithms is proposed for maximum a posteriori (MAP) estimation. A subset of scale parameters is driven to a small lower bound in the course of inference, with the effect of pruning the corresponding spurious components. We demonstrate the efficacy and robustness of our algorithms by performing extensive experiments on synthetic data, the swimmer dataset, a music decomposition example, and a stock price prediction task.
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automatic relevance determination in nonnegative matrix factorization with the beta divergence
arXiv: Machine Learning, 2011Co-Authors: Cedric FevotteAbstract:This paper addresses the estimation of the latent dimensionality in nonnegative matrix factorization (NMF) with the \beta-divergence. The \beta-divergence is a family of cost functions that includes the Squared Euclidean Distance, Kullback-Leibler and Itakura-Saito divergences as special cases. Learning the model order is important as it is necessary to strike the right balance between data fidelity and overfitting. We propose a Bayesian model based on automatic relevance determination in which the columns of the dictionary matrix and the rows of the activation matrix are tied together through a common scale parameter in their prior. A family of majorization-minimization algorithms is proposed for maximum a posteriori (MAP) estimation. A subset of scale parameters is driven to a small lower bound in the course of inference, with the effect of pruning the corresponding spurious components. We demonstrate the efficacy and robustness of our algorithms by performing extensive experiments on synthetic data, the swimmer dataset, a music decomposition example and a stock price prediction task.
