In this paper, we consider the problem of dictionary learning for sparse representations. Several algorithms dealing with this problem can be found in the literature. One of them, introduced by Sezer et al. in [1] opt...
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ISBN:
(纸本)9781424442959
In this paper, we consider the problem of dictionary learning for sparse representations. Several algorithms dealing with this problem can be found in the literature. One of them, introduced by Sezer et al. in [1] optimizes a dictionary made up of the union of orthonormal bases. In this paper, we propose a probabilistic interpretation of Sezer's algorithm and suggest a novel optimization procedure based on the EM algorithm. Comparisons of the performance in terms of missed detection rate show a clear superiority of the proposed approach.
We recently introduced the high-resolution nonnegative matrix factorization (HR-NMF) model for analyzing mixtures of non-stationary signals in the time-frequency domain, and highlighted its capability to both reach hi...
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ISBN:
(纸本)9781479903573
We recently introduced the high-resolution nonnegative matrix factorization (HR-NMF) model for analyzing mixtures of non-stationary signals in the time-frequency domain, and highlighted its capability to both reach high spectral resolution and reconstruct high quality audio signals. In order to estimate the model parameters and the latent components, we proposed to resort to an expectation-maximization (EM) algorithm based on a Kalman filter/smoother. The approach proved to be appropriate for modeling audio signals in applications such as source separation and audio inpainting. However, its computational cost is high, dominated by the Kalman filter/smoother, and may be prohibitive when dealing with high-dimensional signals. In this paper, we consider two different alternatives, using the variational Bayesian EM algorithm and two mean-field approximations. We show that, while significantly reducing the complexity of the estimation, these novel approaches do not alter its quality.
This study investigates the price structure of urban housing markets comparing the Black-Scholes model and Merton's jump diffusion model with the expectation-maximization algorithm. As price jump information is hi...
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This study investigates the price structure of urban housing markets comparing the Black-Scholes model and Merton's jump diffusion model with the expectation-maximization algorithm. As price jump information is hidden within the price change itself, an appropriate method must be used to deal with the hidden data. We check the validity of models in six cities using interval-ahead Monte Carlo simulations. We find that the jump diffusion model is well suited for analyzing the housing market and price structure in most cases.
We discuss inverted exponentiated Rayleigh distribution under progressive first-failure censoring. Maximum likelihood and Bayes estimates of unknown parameters are obtained. An expectation-maximization algorithm is us...
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We discuss inverted exponentiated Rayleigh distribution under progressive first-failure censoring. Maximum likelihood and Bayes estimates of unknown parameters are obtained. An expectation-maximization algorithm is used for computing maximum likelihood estimates. Asymptotic intervals are constructed from the observed Fisher information matrix. Bayes estimates of unknown parameters are obtained under the squared error loss function. We construct highest posterior density intervals based on importance sampling. Different predictors and prediction intervals of censored observations are discussed. A Monte Carlo simulations study is performed to compare different methods. Finally, three real data sets are analyzed for illustration purposes.
The development of a new method for testing the association of genetic markers with disease is presented. This approach is applicable when sampling nuclear families with one or more affected siblings and when neither,...
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