  We design a metric learning approach that aims to address computational
challenges that yield from modeling human outcomes from ambulatory real-life
data. The proposed metric learning is based on a Siamese neural network (SNN)
that learns the relative difference between pairs of samples from a target user
and non-target users, thus being able to address the scarcity of labelled data
from the target. The SNN further minimizes the Wasserstein distance of the
learned embeddings between target and non-target users, thus mitigating the
distribution mismatch between the two. Finally, given the fact that the base
rate of focal behaviors is different per user, the proposed method approximates
the focal base rate based on labelled samples that lay closest to the target,
based on which further minimizes the Wasserstein distance. Our method is
exemplified for the purpose of hourly stress classification using real-life
multimodal data from 72 dating couples. Results in few-shot and one-shot
learning experiments indicate that proposed formulation benefits stress
classification and can help mitigate the aforementioned challenges.
