Reducing the dimensionality of word embeddings I trained word embeddings with 300 dimensions. Now, I would like to have word embeddings with 50 dimensions: is it better to retrain the word embeddings with 50 dimensions, or can I use some dimensionality reduction method to scale the word embeddings with 300 dimensions down to 50 dimensions?
There is a paper on this subject calle


  Simple and Effective Dimensionality Reduction for Word Embeddings, Vikas Raunak


You can read it here

You can also find the implementation here

In my opinion it works quite well