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Riemannian Optimization for Skip-Gram Negative Sampling
[article]
2017
arXiv
pre-print
Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in "word2vec" software, is usually optimized by stochastic gradient descent. However, the optimization of SGNS objective can be viewed as a problem of searching for a good matrix with the low-rank constraint. The most standard way to solve this type of problems is to apply Riemannian optimization framework to optimize the SGNS objective over the manifold of required low-rank matrices. In this paper, we
arXiv:1704.08059v1
fatcat:l3gr73n6nbbjvoz7fwxt262q4q