Probabilistic Clustering using Maximal Matrix Norm Couplings
Name
1810.04738.pdf
Description
Accepted version
Size
418.53 KB
Format
Unknown
Checksum (MD5)
1b53a9166709920d6281dc58d99b05ff
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/allerton.2018.8635939
