Advancing Non-Negative Latent Factorization of Tensors With Diversified Regularization Schemes

Document Type

Article

Publication Date

1-1-2022

Abstract

Dynamic relationships are frequently encountered in big data and services computing-related applications, like dynamic data of user-side QoS in Web services. They are modeled into a high-dimensional and sparse (HiDS) tensor, which contain rich knowledge regarding temporal patterns. A non-negative latent factorization of tensors (NLFT) model is very effective in extracting such patterns from an HiDS tensor. However, it commonly suffers from overfitting with improper regularization schemes. To address this issue, this article investigates NLFT models with diversified regularization schemes. Six regularized NLFT models, i.e., L2, L1 L2,L1, elastic net, log, dropout, and swish-regularized ones, are proposed and carefully investigated. Moreover, owing to their diversified regularization designs, they possess strong model diversity to achieve an effective ensemble. Empirical studies on HiDS QoS tensors from real applications demonstrate that compared with state-of-the-art models, the proposed ones better describe the temporal patterns hidden in an HiDS tensor, thereby achieving significantly higher prediction accuracy for missing data. Moreover, their ensemble further outperforms each of them in terms of prediction accuracy for missing QoS data.

Identifier

85083785372 (Scopus)

Publication Title

IEEE Transactions on Services Computing

External Full Text Location

https://doi.org/10.1109/TSC.2020.2988760

e-ISSN

19391374

First Page

1334

Last Page

1344

Issue

3

Volume

15

Grant

61602352

Fund Ref

National Natural Science Foundation of China

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