Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105734
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 |
Issue Date: | 2016 | Source: | IEEE access, 2016, v. 4, p. 2649-2655 | 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. | Keywords: | Big Data Inherently Non-negative Latent Factors Non-negative Big Sparse Matrices Non-negativity |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE access | EISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2016.2556680 | Rights: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Posted with permission of the publisher. The following publication X. Luo, M. Zhou, M. Shang, S. Li and Y. Xia, "A Novel Approach to Extracting Non-Negative Latent Factors From Non-Negative Big Sparse Matrices," in IEEE Access, vol. 4, pp. 2649-2655, 2016 is available at https://doi.org/10.1109/ACCESS.2016.2556680. |
Appears in Collections: | Journal/Magazine Article |
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Luo_Novel_Approach_Extracting.pdf | 9.39 MB | Adobe PDF | View/Open |
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