Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62027
Title: A novel approach to extracting non-negative latent factors from non-negative big sparse matrices
Authors: Luo, X
Zhou, M
Shang, M
Li, S 
Xia, Y
Keywords: Big Data
Inherently Non-negative
Latent Factors
Non-negative Big Sparse Matrices
Non-negativity
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2016, v. 4 , 7457202, p. 2649-2655 How to cite?
Journal: IEEE access 
Abstract: An inherently non-negative latent factor model is proposed to extract non-negative latent factors from non-negative big sparse matrices efficiently and effectively. A single-element-dependent sigmoid function connects output latent factors with decision variables, such that non-negativity constraints on the output latent factors are always fulfilled and thus successfully separated from the training process with respect to the decision variables. Consequently, the proposed model can be easily and fast built with excellent prediction accuracy. Experimental results on an industrial size sparse matrix are given to verify its outstanding performance and suitability for industrial applications.
URI: http://hdl.handle.net/10397/62027
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2016.2556680
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