Abstract--Plenty of methods have been proposed in order to discover latent variables (features) in data sets. Such approaches include the principal component analysis (PCA), indepe...
In this paper general solutions for Nonlinear Nonnegative Component Analysis for data representation and recognition are proposed. That is, motivated by a combination of the Nonne...
Kernel nonnegative matrix factorization (KNMF) is a recent kernel extension of NMF, where matrix factorization is carried out in a reproducing kernel Hilbert space (RKHS) with a f...
In this paper, we restudy the non-convex data factorization problems (regularized or not, unsupervised or supervised), where the optimization is confined in the nonnegative orthan...
A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank no...
Farial Shahnaz, Michael W. Berry, V. Paul Pauca, R...