# Row-clustering of a Point Process-valued Matrix

arXiv: https://arxiv.org/abs/2110.01207 · HTML: https://mlsanity.com/paper/2110.01207/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering](https://mlsanity.com/paper/1701.09177/) (2017)
- [Efficient Non-parametric Bayesian Hawkes Processes](https://mlsanity.com/paper/1810.03730/) (2018)
- [Low-Rank Covariance Function Estimation for Multidimensional Functional Data](https://mlsanity.com/paper/2008.12919/) (2020)
- [Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data](https://mlsanity.com/paper/1402.4732/) (2014)
- [VegasFlow: accelerating Monte Carlo simulation across multiple hardware platforms](https://mlsanity.com/paper/2002.12921/) (2020)
- [Analysis of professional basketball field goal attempts via a Bayesian matrix clustering approach](https://mlsanity.com/paper/2010.08495/) (2020)
- [Level set Cox processes](https://mlsanity.com/paper/1708.06982/) (2017)

## Built on by (most cited first, 1 of 1)

- [Learning under Commission and Omission Event Outliers](https://mlsanity.com/paper/2501.13599/) (2025)

## Abstract (arXiv)

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.