Row-clustering of a Point Process-valued Matrix
This work addresses challenges in analyzing point process data from platforms, but it appears incremental as it builds on existing methods like mixture models and FPCA.
The authors tackled the problem of identifying heterogeneity in structured point process data by proposing a mixture model for clustering rows of a matrix with marked log-Gaussian Cox process entries, demonstrating effectiveness through simulations and real data analysis.
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.