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Dimension Reduction for High-dimensional Small Counts with KL Divergence

Ling, Yurong; Xue, Jinghao; (2022) Dimension Reduction for High-dimensional Small Counts with KL Divergence. In: Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022). (pp. pp. 1-11). UAI (In press). Green open access

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Abstract

Dimension reduction for high-dimensional count data with a large proportion of zeros is an important task in various applications. As a large number of dimension reduction methods rely on the proximity measure, we develop a dissimilarity measure that is well-suited for small counts based on the Kullback-Leibler divergence. We compare the proposed measure with other widely used dissimilarity measures and show that the proposed one has superior discrimination ability when applied to high-dimensional count data having an excess of zeros. Extensive empirical results, on both simulated and publicly-available real-world datasets that contain many zeros, demonstrate that the proposed dissimilarity measure can improve a wide range of dimension reduction methods.

Type: Proceedings paper
Title: Dimension Reduction for High-dimensional Small Counts with KL Divergence
Event: The 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022)
Location: Eindhoven, The Netherlands
Dates: 1st-5th August 2022
Open access status: An open access version is available from UCL Discovery
Publisher version: https://openreview.net/forum?id=BhzEFwLj5l5
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 > 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
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10150456
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