# Smooth and probabilistic PARAFAC model with auxiliary covariates

arXiv: https://arxiv.org/abs/2104.05184 · HTML: https://mlsanity.com/paper/2104.05184/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2021.

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

- [BAMITA: Bayesian Multiple Imputation for Tensor Arrays](https://mlsanity.com/paper/2410.23412/) (2024)

## Abstract (arXiv)

In immunological and clinical studies, matrix-valued time-series data clustering is increasingly popular. Researchers are interested in finding low-dimensional embedding of subjects based on potentially high-dimensional longitudinal features and investigating relationships between static clinical covariates and the embedding. These studies are often challenging due to high dimensionality, as well as the sparse and irregular nature of sample collection along the time dimension. We propose a smoothed probabilistic PARAFAC model with covariates (SPACO) to tackle these two problems while utilizing auxiliary covariates of interest. We provide intensive simulations to test different aspects of SPACO and demonstrate its use on an immunological data set from patients with SARs-CoV-2 infection.