Institute of Electrical & Electronic Engineers
Nonnegative Matrix Factorization (NMF) is one of the most promising techniques to reduce the dimensionality of the data. This paper compares the method with other popular matrix decomposition approaches for various pattern analysis tasks. Among others, NMF has been also widely applied for clustering and latent feature extraction. Several types of the objective functions have been used for NMF in the literature. Instead of minimizing the common Euclidean Distance (EucD) error, the authors review an alternative method that maximizes the correntropy similarity measure to produce the factorization.