A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Group Sparsity and Graph Regularized Semi-Nonnegative Matrix Factorization with Discriminability for Data Representation
2017
Entropy
Semi-Nonnegative Matrix Factorization (Semi-NMF) , as a variant of NMF, inherits the merit of parts-based representation of NMF and possesses the ability to process mixed sign data, which has attracted extensive attention. However, standard Semi-NMF still suffers from the following limitations. First of all, Semi-NMF fits data in a Euclidean space, which ignores the geometrical structure in the data. What's more, Semi-NMF does not incorporate the discriminative information in the learned
doi:10.3390/e19120627
fatcat:l5ipuakwuvh5fk2wwz6sasnknu