1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2022
Adaptive Weighted Nonnegative Matrix Factorization for Robust Feature Representation
Tingting Shen, Junhang Li, Can Tong +4
Nonnegative matrix factorization (NMF) has been widely used to dimensionality reduction in machine learning. However, the traditional NMF does not properly handle outliers, so that…
cs.CV2022★ 1 cited
Subspace Nonnegative Matrix Factorization for Feature Representation
Junhang Li, Jiao Wei, Can Tong +6
Traditional nonnegative matrix factorization (NMF) learns a new feature representation on the whole data space, which means treating all features equally. However, a subspace is of…
cs.LG2021
An Entropy Weighted Nonnegative Matrix Factorization Algorithm for Feature Representation
Jiao Wei, Can Tong, Bingxue Wu +4
Nonnegative matrix factorization (NMF) has been widely used to learn low-dimensional representations of data. However, NMF pays the same attention to all attributes of a data point…