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Dimension reduction for data with heterogeneous missingness

Ling, Y; Liu, Z; Xue, J; (2022) Dimension reduction for data with heterogeneous missingness. In: (Proceedings) Conference on Uncertainty in Artificial Intelligence (UAI 2021). MLResearchPress (In press).

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Abstract

Dimension reduction plays a pivotal role in analysing high-dimensional data. However, obser- vations with missing values present serious difficul- ties in directly applying standard dimension reduc- tion techniques. As a large number of dimension reduction approaches are based on the Gram ma- trix, we first investigate the effects of missingness on dimension reduction by studying the statisti- cal 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 re- sults, 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.

Type: Proceedings paper
Title: Dimension reduction for data with heterogeneous missingness
Event: Conference on Uncertainty in Artificial Intelligence (UAI 2021)
Dates: 27 July 2021 - 30 July 2021
Publisher version: http://proceedings.mlr.press
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10135636
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