Improved coupled tensor factorization with its applications in health data analysis

Qing Wu, Jie Wang, Jin Fan, Gang Xu, Jia Wu, Blake Johnson, Xingfei Li, Quan Do, Ruiquan Ge

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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Abstract

Coupled matrix and tensor factorizations have been successfully used in many data fusion scenarios where datasets are assumed to be exactly coupled. However, in the real world, not all the datasets share the same factor matrices, which makes joint analysis of multiple heterogeneous sources challenging. For this reason, approximate coupling or partial coupling is widely used in real-world data fusion, with exact coupling as a special case of these techniques. However, to fully address the challenge of tensor factorization, in this paper, we propose two improved coupled tensor factorization methods: one for approximately coupled datasets and the other for partially coupled datasets. A series of experiments using both simulated data and three real-world datasets demonstrate the improved accuracy of these approaches over existing baselines. In particular, when experiments on MRI data is conducted, the performance of our method is improved even by 12.47% in terms of accuracy compared with traditional methods.
Original languageEnglish
Article number1574240
Pages (from-to)1-16
Number of pages16
JournalComplexity
Volume2019
DOIs
Publication statusPublished - 2019

Bibliographical note

Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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