# word2vec Skip-Gram with Negative Sampling is a Weighted Logistic PCA

arXiv: https://arxiv.org/abs/1705.09755 · HTML: https://mlsanity.com/paper/1705.09755/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2017.

## Built on by (most cited first, 2 of 2)

- [Learning distributed representations with efficient SoftMax normalization](https://mlsanity.com/paper/2303.17475/) (2023)
- [A Statistical Inference Framework for PMI Estimation and SGNS Word Embeddings](https://mlsanity.com/paper/2610.05058/) (2026)

## Abstract (arXiv)

We show that the skip-gram formulation of word2vec trained with negative sampling is equivalent to a weighted logistic PCA. This connection allows us to better understand the objective, compare it to other word embedding methods, and extend it to higher dimensional models.