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).
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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 |
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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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