Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization
Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization is a scholarly work by Andrzej Cichocki, published in 2018 in ''IEEE Access''. The main subjects of the publication include non-negative matrix factorization, cluster analysis, feature engineering, deep learning, artificial intelligence, computer science, matrix, architecture, factorization, facial recognition system, feature, hierarchical clustering, matrix decomposition, pattern recognition, combing, and algorithm. To fill this gap, authors propose a deep nsNMF method coined by the fact that it possesses a deeper architecture compared with standard nsNMF.