Row-clustering of a Point Process-valued MatrixDownload PDF

May 21, 2021 (edited Oct 28, 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Expectation-Solution Algorithm, Functional Principal Component Analysis, Marked Point Processes, Model-based Clustering, Semiparametric Model
  • TL;DR: We propose a mixture model of multi-level marked point processes for clustering repeatedly observed marked event sequences
  • Abstract: 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.
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