Paper Info
Title
Performance evaluation of methods for integrative dimension reduction.
Abstract
Dimension reduction (DR) methods play an inevitable role in analyzing and visualizing high-dimensional multi-source data. In the recent decades many variants of these methods have been developed in various disciplines and domains. Due to the diversity and an ever-increasing number of developed techniques, choosing the right method for the given problem is a difficult task. In this study we benchmark 87 methods for integrative dimension reduction of mRNA expression and DNA methylation data, which is a common problem in biology and medicine. Our ranking is obtained based on four main factors: quality of dimension reduction (local, global, and local-global neighborhood preservation), clustering quality, speed and sensitivity to input parameters on multiple datasets generated by InterSIM (a semi-realistic multi-source data simulator in the genomics domain). The results are later validated on a real dataset for breast cancer through visual evaluation metrics such as co-ranking matrices, inspection of true cancer sub-types in two-dimensional projections, and LCMC curves. We also demonstrate the relationship between the methods via network inference. The findings in this study can be useful in algorithm selection and planning of experimental design in multi-source data analysis.
Year
DOI
Venue
2019
10.1016/j.ins.2019.04.041
Information Sciences
Keywords
Field
DocType
Performance evaluation,Dimension reduction,Multi-source data,Data fusion,Matrix factorization
Dimensionality reduction,Ranking,Inference,Genomics,Artificial intelligence,Algorithm Selection,Cluster analysis,Mathematics,Machine learning
Journal
Volume
ISSN
Citations 
493
0020-0255
2
PageRank 
References 
Authors
0.46
0
2
Name
Order
Citations
PageRank
Hadi Fanaee-T1758.55
Magne Thoresen2124.14