Row-clustering of a Point Process-valued Matrix
Machine Learning
2021-11-18 v2 Machine Learning
Methodology
Abstract
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.
Cite
@article{arxiv.2110.01207,
title = {Row-clustering of a Point Process-valued Matrix},
author = {Lihao Yin and Ganggang Xu and Huiyan Sang and Yongtao Guan},
journal= {arXiv preprint arXiv:2110.01207},
year = {2021}
}