Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data

Document Type

Article

Publication Date

4-1-2018

Abstract

Generating highly accurate predictions for missing quality-of-service (QoS) data is an important issue. Latent factor (LF)-based QoS-predictors have proven to be effective in dealing with it. However, they are based on first-order solvers that cannot well address their target problem that is inherently bilinear and nonconvex, thereby leaving a significant opportunity for accuracy improvement. This paper proposes to incorporate an efficient second-order solver into them to raise their accuracy. To do so, we adopt the principle of Hessian-free optimization and successfully avoid the direct manipulation of a Hessian matrix, by employing the efficiently obtainable product between its Gauss–Newton approximation and an arbitrary vector. Thus, the second-order information is innovatively integrated into them. Experimental results on two industrial QoS datasets indicate that compared with the state-of-the-art predictors, the newly proposed one achieves significantly higher prediction accuracy at the expense of affordable computational burden. Hence, it is especially suitable for industrial applications requiring high prediction accuracy of unknown QoS data.

Identifier

85018486012 (Scopus)

Publication Title

IEEE Transactions on Cybernetics

External Full Text Location

https://doi.org/10.1109/TCYB.2017.2685521

e-ISSN

21682275

ISSN

21682267

PubMed ID

28422674

First Page

1216

Last Page

1228

Issue

4

Volume

48

Grant

61370150

Fund Ref

Royal Society

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