A topographical nonnegative matrix factorization algorithm
Nicoleta Rogovschi, Lazhar Labiod, Mohamed Nadif · 2013
We explore in this paper a novel topological organization algorithm for data clustering and visualization named TPNMF. It leads to a clustering of the data, as well as the projection of the clusters on a two-dimensional grid while preserving the topological order of the initial data. The proposed algorithm is based on a NMF (Nonnegative Matrix Factorization) formalism using a neighborhood function which take into account the topological order of the data. TPNMF was validated on variant real datasets and the experimental results show a good quality of the topological ordering and homogenous clustering.