# Non-Negative Matrix Factorization with Sinkhorn Distance

@inproceedings{Qian2016NonNegativeMF, title={Non-Negative Matrix Factorization with Sinkhorn Distance}, author={Wei Qian and Bin Hong and Deng Cai and X. He and X. Li}, booktitle={IJCAI}, year={2016} }

Non-negative Matrix Factorization (NMF) has received considerable attentions in various areas for its psychological and physiological interpretation of naturally occurring data whose representation may be parts-based in the human brain. Despite its good practical performance, one shortcoming of original NMF is that it ignores intrinsic structure of data set. On one hand, samples might be on a manifold and thus one may hope that geometric information can be exploited to improve NMF's performance… Expand

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