Row-Clustering of a Point Process-Valued Matrix
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.
Cite
Text
Yin et al. "Row-Clustering of a Point Process-Valued Matrix." Neural Information Processing Systems, 2021.Markdown
[Yin et al. "Row-Clustering of a Point Process-Valued Matrix." Neural Information Processing Systems, 2021.](https://mlanthology.org/neurips/2021/yin2021neurips-rowclustering/)BibTeX
@inproceedings{yin2021neurips-rowclustering,
title = {{Row-Clustering of a Point Process-Valued Matrix}},
author = {Yin, Lihao and Xu, Ganggang and Sang, Huiyan and Guan, Yongtao},
booktitle = {Neural Information Processing Systems},
year = {2021},
url = {https://mlanthology.org/neurips/2021/yin2021neurips-rowclustering/}
}