AllenCellModeling / pytorch_integrated_cell

Integrated Cell project implemented in pytorch
43 stars 11 forks source link

Results: latent space truncation #70

Open donovanr opened 4 years ago

donovanr commented 4 years ago

Issue summary

hard truncation of latent space and look at reconstructions

Details

Again by analogy with PCA, it should be possible to truncate the latent space (force all dimensions beyond some chosen zn to go to zero) and still get pretty decent reconstructions, if they are really sorted in order in a meaningful way such that the later dimensions are “noise”. This could also be given a simple reality check by choosing an intermediate value of beta (say 0.4 or 0.5), truncating at some modest number of dimensions (I’m gonna say 10, but this should be done empirically) and then examining how much the reconstruction loss suffers by truncation. The reason this is useful is because it is much easier to do meaningful comparative statistics (cell population A is different from or indistinguishable from cell population B) in fewer dimensions than 512.

TODO