Dimension reduction for data with heterogeneous missingness
File(s)
Published version
Author(s)
Ling, Yurong
Liu, Zijing
Xue, Jing-Hao
Type
Conference Paper
Abstract
Dimension reduction plays a pivotal role in analysing high-dimensional data. However, observations with missing values present serious difficulties in directly applying standard dimension reduction techniques. As a large number of dimension reduction approaches are based on the Gram matrix, we first investigate the effects of missingness on dimension reduction by studying the statistical properties of the Gram matrix with or without missingness, and then we present a bias-corrected Gram matrix with nice statistical properties under heterogeneous missingness. Extensive empirical results, on both simulated and publicly available real datasets, show that the proposed unbiased Gram matrix can significantly improve a broad spectrum of representative dimension reduction approaches.
Date Issued
2021-07-30
Date Acceptance
2021-05-12
Citation
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021, 161, pp.1310-1320
Publisher
PMLR
Start Page
1310
End Page
1320
Journal / Book Title
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence
Volume
161
Copyright Statement
© The Author(s). This work is licensed under Creative Commons Attribution 4.0 Inter-
national License.
national License.
License URL
Identifier
https://proceedings.mlr.press/v161/ling21a.html
Source
Thirty-Seventh Conference on Uncertainty in Artificial Intelligence
Publication Status
Published
Start Date
2021-07-27
Finish Date
2021-07-30
Coverage Spatial
Online
Date Publish Online
2021-07-30