Neighbor embedding with non-negative matrix factorization for image prediction

The paper studies several non-negative matrix factorization methods with nearest neighbors constrained dictionaries for image prediction. The methods considered include the multiplicative update algorithm, the projected gradient algorithm, as well as the graph-regularized NMF solution which aims at taking into account the geometrical structure of the input data. The Intra prediction problem based on these NMF solutions amounts to a neighbor embedding problem. Both prediction and rate-distortion performances are then given in comparison with other neighbor embedding methods like locally linear embedding (LLE) and locally linear embedding with low dimensional neigborhood representation (LLE-LDNR).