TY - JOUR
T1 - Improved coupled tensor factorization with its applications in health data analysis
AU - Wu, Qing
AU - Wang, Jie
AU - Fan, Jin
AU - Xu, Gang
AU - Wu, Jia
AU - Johnson, Blake
AU - Li, Xingfei
AU - Do, Quan
AU - Ge, Ruiquan
N1 - 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.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85062350105&partnerID=8YFLogxK
U2 - 10.1155/2019/1574240
DO - 10.1155/2019/1574240
M3 - Article
SN - 1076-2787
VL - 2019
SP - 1
EP - 16
JO - Complexity
JF - Complexity
M1 - 1574240
ER -