Nonnegative Matrix Factorization:Model,Algorithms and Applications
Xiangsu Zhang · Chongqing Shifan Daxue xuebao. Ziran kexue ban · 2013
Nonnegative Matrix Factorization(NMF)is becoming one of the most popular models in data mining society recently.NMF can extract hidden patterns from a series of high-dimensional vectors automatically,and has been applied for dimensional reduction,unsupervised learning(image processing,clustering and co-clustering,etc.)and prediction successfully.This paper surveys NMF in terms of the research history,model formulation,algorithms and applications.In summary,NMF has good interpretability,is very flexible,has a close relationship with the existing state of the art unsupervised learning models and a variety of applications.In addition,as a developing technology,there are still many interesting open issues remained unsolved and waiting for research from different perspectives.