A Novel Approach to Extracting Non-Negative Latent Factors from Non-Negative Big Sparse Matrices

Document Type

Article

Publication Date

1-1-2016

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.

Identifier

84979844540 (Scopus)

Publication Title

IEEE Access

External Full Text Location

https://doi.org/10.1109/ACCESS.2016.2556680

e-ISSN

21693536

First Page

2649

Last Page

2655

Volume

4

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