Home » Source Code » This is a 3D visualization of how the Expectation Maximization algorithm learns a Gaussian Mixture Model for 3-dimensional data.

This is a 3D visualization of how the Expectation Maximization algorithm learns a Gaussian Mixture Model for 3-dimensional data.

anqbllh
2012-10-25 05:52:53
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Traditional single particle reconstruction methods use either the Fourier or the delta function basis to represent the particle density map. We propose a more flexible algorithm that adaptively chooses the basis based on the data. Because the basis adapts to the data, the reconstruction resolution and signal-to-noise ratio (SNR) is improved compared to a reconstruction with a fixed basis. Moreover, the algorithm automatically masks the particle, thereby separating it from the background. This eliminates the need for ad-hoc filtering or masking in the refinement loop. The algorithm is formulated in a Bayesian maximum-a-posteriori framework and uses an efficient optimization algorithm for the maximization. Evaluations using simulated and actual cryogenic electron microscopy data show resolution and SNR improvements as well as the effective masking of particle from background.
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Name Size Date
EM_GMM_3d.m12.29 kB2012-03-28 14:41
license.txt1.30 kB2012-03-28 14:41
...
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This is a 3D visualization of how the Expectation Maximization algorithm learns a Gaussian Mixture Model for 3-dimensional data. (5.09 kB)

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