Probabilistic PARAFAC2

TL;DR

This paper introduces two probabilistic formulations of the PARAFAC2 model, enhancing robustness to noise and aiding in model order determination for multi-way data analysis.

Contribution

It develops novel probabilistic approaches for PARAFAC2 with variational inference, addressing orthogonality constraints and improving robustness over traditional methods.

Findings

Probabilistic PARAFAC2 is more noise-robust.

The approach outperforms conventional PARAFAC2 in real data.

It effectively handles model order misspecification.

Abstract

The PARAFAC2 is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example because of differences in signal sampling or batch sizes. A fully probabilistic treatment of the PARAFAC2 is desirable in order to improve robustness to noise and provide a well founded principle for determining the number of factors, but challenging because the factor loadings are constrained to be orthogonal. We develop two probabilistic formulations of the PARAFAC2 along with variational procedures for inference: In the one approach, the mean values of the factor loadings are orthogonal leading to closed form variational updates, and in the other, the factor loadings themselves are orthogonal using a matrix Von Mises-Fisher distribution. We contrast our probabilistic formulation to the conventional direct fitting algorithm based on maximum likelihood. On simulated data and real fluorescence spectroscopy and gas chromatography-mass spectrometry data, we compare our approach to the conventional PARAFAC2 model estimation and find that the probabilistic formulation is more robust to noise and model order misspecification. The probabilistic PARAFAC2 thus forms a promising framework for modeling multi-way data accounting for uncertainty.