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Probabilistic Clustering using Maximal Matrix Norm Couplings
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Probabilistic Clustering using Maximal Matrix Norm Couplings
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1810.04738.pdf
Description
Accepted version
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418.53 KB
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sword-2019-07-08T18:16:21.original.xml (130 B)
Original SWORD entry document
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Author(s)
Qiu, David
•
Makur, Anuran
•
Zheng, Lizhong
Date Issued
October 2018
Publisher
IEEE
Citation
Qiu, David, Makur, Anuran and Zheng, Lizhong. 2018. "Probabilistic Clustering using Maximal Matrix Norm Couplings."
Version
Author's final manuscript
Abstract
© 2018 IEEE. In this paper, we present a local information theoretic approach to explicitly learn probabilistic clustering of a discrete random variable. Our formulation yields a convex maximization problem for which it is NP-hard to find the global optimum. In order to algorithmically solve this optimization problem, we propose two relaxations that are solved via gradient ascent and alternating maximization. Experiments on the MSR Sentence Completion Challenge, MovieLens 100K, and Reuters21578 datasets demonstrate that our approach is competitive with existing techniques and worthy of further investigation.
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Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
http://creativecommons.org/licenses/by-nc-sa/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/137715
DOI of Published Version
10.1109/allerton.2018.8635939
