Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/584467
Title: A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices
Authors: Xin Luo;Mengchu Zhou;Mingsheng Shang;Shuai Li;Yunni Xia
subject: Latent Factors|Non-negative Big Sparse Matrices|Big Data|Inherently Non-negative|Non-negativity
Year: 2016
Publisher: IEEE
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.
Description: 
URI: http://localhost/handle/Hannan/165771
http://localhost/handle/Hannan/584467
ISSN: 2169-3536
volume: 4
Appears in Collections:2016

Files in This Item:
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Title: A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices
Authors: Xin Luo;Mengchu Zhou;Mingsheng Shang;Shuai Li;Yunni Xia
subject: Latent Factors|Non-negative Big Sparse Matrices|Big Data|Inherently Non-negative|Non-negativity
Year: 2016
Publisher: IEEE
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.
Description: 
URI: http://localhost/handle/Hannan/165771
http://localhost/handle/Hannan/584467
ISSN: 2169-3536
volume: 4
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7457202.pdf9.49 MBAdobe PDFThumbnail
Preview File
Title: A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices
Authors: Xin Luo;Mengchu Zhou;Mingsheng Shang;Shuai Li;Yunni Xia
subject: Latent Factors|Non-negative Big Sparse Matrices|Big Data|Inherently Non-negative|Non-negativity
Year: 2016
Publisher: IEEE
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.
Description: 
URI: http://localhost/handle/Hannan/165771
http://localhost/handle/Hannan/584467
ISSN: 2169-3536
volume: 4
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7457202.pdf9.49 MBAdobe PDFThumbnail
Preview File