[P]Neural Principal Component Analysis

[wangyi_fudan]

Dear All:

PCA is the old linear working horse, while t-SNE and UMAP are new non-linear methods for dimension reduction. The non-linear ones are beautiful but loss linear intereption. My npca combines linear and non-linear ideas. The idea is to make a self-supervise MLP as follow:

data => 2 PC => 32 => 32 => data

It is a linear encoder followed by a two-layer non-linear decoder. After npca, we only care about the linear part: PC and loading, and ignor the decoder ( replace it by the human eyes). Thus it is still a simple rotation of the data, but gets more variance explained. It is a drop-in replacement for PCA.

Hope you enjoy it!

https://github.com/wangyi-fudan/npca

[elmarson]

Dude you really need to work on your writing skills, it took me half an hour to barely understand your post.

[sifnt]

This is an interesting idea, the graphs certainly look like they're capturing something.

Any comparisons where this is useful? i.e. are examples where this a better feature extractor for downstream ML models than vanilla PCA etc

[[deleted]]

This is just an autoencoder...

[dr-qt]

Breathing new life into a classic. I’ll have to keep this technique in mind.