1 research outputs found
Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization
Nonsmooth Nonnegative Matrix Factorization (nsNMF) is capable of producing
more localized, less overlapped feature representations than other variants of
NMF while keeping satisfactory fit to data. However, nsNMF as well as other
existing NMF methods is incompetent to learn hierarchical features of complex
data due to its shallow structure. To fill this gap, we propose a deep nsNMF
method coined by the fact that it possesses a deeper architecture compared with
standard nsNMF. The deep nsNMF not only gives parts-based features due to the
nonnegativity constraints, but also creates higher-level, more abstract
features by combing lower-level ones. The in-depth description of how deep
architecture can help to efficiently discover abstract features in dnsNMF is
presented. And we also show that the deep nsNMF has close relationship with the
deep autoencoder, suggesting that the proposed model inherits the major
advantages from both deep learning and NMF. Extensive experiments demonstrate
the standout performance of the proposed method in clustering analysis