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

Xin Luo, Mengchu Zhou, Mingsheng Shang, Shuai Li, Yunni Xia

Research output: Journal article publicationJournal articleAcademic researchpeer-review

65 Citations (Scopus)


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.
Original languageEnglish
Article number7457202
Pages (from-to)2649-2655
Number of pages7
JournalIEEE Access
Publication statusPublished - 1 Jan 2016


  • Big Data
  • Inherently Non-negative
  • Latent Factors
  • Non-negative Big Sparse Matrices
  • Non-negativity

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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