Sadler, R. Blum, Array Processing in Non-Gaussian Noise with the EM Algorithm, International Conference on Acoustics, Speech, and Signal Processing, pages 1997-2000, Seattle, Washington, USA, 1998.R. Kozick, B. Sadler, R. Blum, Array processing in non-Gaussian noise with the EM algorithm,...
In the context of our proposed algorithm, the main purpose of the pilots is the enablement of sufficient quality initialization of the EM iterations so that the probability of convergence to a local maximum is minimized. To that end, we propose an initialization scheme based on a small number ...
Couvreur, “Neural networks and statistics: a naive comparison”, to appear in JORBEL: Belgian Journal of Operations Research, Statistics and Computer Sciences, 1997. Google Scholar A.P. Dempster, N.M. Laird and D.B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm”,...
To do so, the sample image area was divided into two parts using the spatial k-means algorithm; one of the clusters corresponding to the tissue sample and the other – to the sample-free matrix-covered surface of the glass slide. The mapping of the clusters to the sample and the ...
The EM algorithm is a popular iterative algorithm for finding maximum likelihood estimates from incomplete data. However, the drawback of the EM algorithm is to converge slowly when the proportion of missing data is large. In order to speed up the convergence of the EM algorithm, we propose th...
Our work mainly differs from DPM-EM in several ways. Firstly, they only considered a maximum likelihood approach to the mixture model whereas we used a Bayesian approach. Secondly, their proposed solution is based on the EM algorithm, whereas we used the MM algorithm within a variational ...
Given that minimizing \(-\log p({{{\bf{x}}})\) directly is intractable in general, our approach for training is to approximately minimize the log-likelihood based on the ideas behind the Expectation-Maximization (EM) algorithm. Specifically, we work with the analog of the complete-data ...
Image data are ideally recorded in super-resolution mode as multi-frame exposures of each region of interest. Use of the super-resolution algorithm means that the “4 k” chip (3838 × 3710 pixels) of a K2 Summit DED records “8 k” frames (7676 × 7420 pixels). Subsequently down...
The data-science revolution is poised to transform the way photonic systems are simulated and designed. Photonic systems are, in many ways, an ideal substrate for machine learning: the objective of much of computational electromagnetics is the capture of nonlinear relationships in high-dimensional spac...
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