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Non-negative matrix factorization based on maximum entropy principle under sparse constraint for clustering

Zhen-You Wang Yongheng Chen Shengbin Xu
Jul 2026 · International Journal of Data Science and Analysis · Vol 22 · 0 citations · 50 references
Computer Science

TL;DR

The maximum entropy non-negative matrix factorization (MENMF) is proposed to adjust the distance relationship of latent feature representation extracted by NMF from two aspects of enhancing the correlation of neighboring points and reducing the correlation of separated points, so as to restore the local geometric structure of the data.

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