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• W2964144157 abstract "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 are 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 part-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, 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." @default.
• W2964144157 created "2019-07-30" @default.
• W2964144157 creator A5018676117 @default.
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• W2964144157 date "2018-01-01" @default.
• W2964144157 modified "2023-10-06" @default.
• W2964144157 title "Learning the Hierarchical Parts of Objects by Deep Non-Smooth Nonnegative Matrix Factorization" @default.
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• W2964144157 doi "https://doi.org/10.1109/access.2018.2873385" @default.
• W2964144157 hasPublicationYear "2018" @default.
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