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.

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