An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications

Document Type

Article

Publication Date

5-1-2018

Abstract

High-dimensional and sparse (HiDS) matrices are commonly encountered in many big-data-related and industrial applications like recommender systems. When acquiring useful patterns from them, nonnegative matrix factorization (NMF) models have proven to be highly effective owing to their fine representativeness of the nonnegative data. However, current NMF techniques suffer from: 1) inefficiency in addressing HiDS matrices; and 2) constraints in their training schemes. To address these issues, this paper proposes to extract nonnegative latent factors (NLFs) from HiDS matrices via a novel inherently NLF (INLF) model. It bridges the output factors and decision variables via a single-element-dependent mapping function, thereby making the parameter training unconstrained and compatible with general training schemes on the premise of maintaining the nonnegativity constraints. Experimental results on six HiDS matrices arising from industrial applications indicate that INLF is able to acquire NLFs from them more efficiently than any existing method does.

Identifier

85032731287 (Scopus)

Publication Title

IEEE Transactions on Industrial Informatics

External Full Text Location

https://doi.org/10.1109/TII.2017.2766528

ISSN

15513203

First Page

2011

Last Page

2022

Issue

5

Volume

14

Grant

TII-17-1697

Fund Ref

Royal Society

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