  Structured point process data harvested from various platforms poses new
challenges to the machine learning community. By imposing a matrix structure to
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 a real data analysis.
