Tgs91 t1_iy491cv wrote on November 28, 2022 at 5:13 PM

Reply to comment by olmec-akeru in [D] What method is state of the art dimensionality reduction by olmec-akeru

Important piece of this question is whether you want a lossy dim reduction or lossless. With something like PCA you can reconstruct the original dimension. With a deep learning based method, there is some degree of information loss (which could be a good thing since it's supervised information loss / choosing important information to retain). If you want to be able to reconstruct the original inputs, you'd need to also build a generator model that is supervised to reconstruct the original. You can get very high quality reconstructions, but it won't be an exact match of the original.

radarsat1 t1_iy4mskn wrote on November 28, 2022 at 6:43 PM

? if you remove dimensions after PCA, you cannot reconstruct the original data ... well, you get something very blurry in any case.

itsyourboiirow t1_iy5aa1i wrote on November 28, 2022 at 9:18 PM

Correct. But you don't necessarily have to discard the extra dimensions to do PCA.

radarsat1 t1_iy5e7jo wrote on November 28, 2022 at 9:43 PM

but the question is about dimensionality reduction

JustOneAvailableName t1_iy7bqul wrote on November 29, 2022 at 7:34 AM

PCA is also lossy

> You can get very high quality reconstructions, but it won't be an exact match of the original.

With a GAN (among others), a VAE for example is fuzzy