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Title: Row-clustering of a Point Process-valued Matrix
Structured point process data harvested from various platforms poses new challenges to the machine learning community. To cluster repeatedly observed marked point processes, we propose a novel mixture model of multi-level marked point processes for identifying potential heterogeneity in the observed data. Specifically, we study a matrix whose entries are marked log-Gaussian Cox processes and cluster rows of such a matrix. An efficient semi-parametric Expectation-Solution (ES) algorithm combined with functional principal component analysis (FPCA) of point processes is proposed for model estimation. The effectiveness of the proposed framework is demonstrated through simulation studies and real data analyses.  more » « less
Award ID(s):
1854655
NSF-PAR ID:
10340887
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Advances in neural information processing systems
Volume:
34
ISSN:
1049-5258
Page Range / eLocation ID:
20028--20039
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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