mlsanityData to 7 Oct 2026

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

arXiv 1705.09755 ↗First posted 27 May 2017cs.CL stat.MLcited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2017.

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  1. 2023Learning distributed representations with efficient SoftMax normalizationcited by 1