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Structured point process data harvested from various platforms poses newchallenges to the machine learning community. By imposing a matrix structure torepeatedly observed marked point processes, we propose a novel mixture model ofmulti-level marked point processes for identifying potential heterogeneity inthe observed data. Specifically, we study a matrix whose entries are markedlog-Gaussian Cox processes and cluster rows of such a matrix. An efficientsemi-parametric Expectation-Solution (ES) algorithm combined with functionalprincipal component analysis (FPCA) of point processes is proposed for modelestimation. The effectiveness of the proposed framework is demonstrated throughsimulation studies and a real data analysis.

Row-clustering of a Point Process-valued Matrix