A Novel Loss Function Utilizing Wasserstein Distance to Reduce
  Subject-Dependent Noise for Generalizable Models in Affective Computing
Nibraas Khan Mahrukh Tauseef Ritam Ghosh Nilanjan Sarkar
Emotions are an essential part of human behavior that can impact thinking,
decision-making, and communication skills. Thus, the ability to accurately
monitor and identify emotions can be useful in many human-centered applications
such as behavioral training, tracking emotional well-being, and development of
human-computer interfaces. The correlation between patterns in physiological
data and affective states has allowed for the utilization of deep learning
techniques which can accurately detect the affective states of a person.
However, the generalisability of existing models is often limited by the
subject-dependent noise in the physiological data due to variations in a
subject's reactions to stimuli. Hence, we propose a novel cost function that
employs Optimal Transport Theory, specifically Wasserstein Distance, to scale
the importance of subject-dependent data such that higher importance is
assigned to patterns in data that are common across all participants while
decreasing the importance of patterns that result from subject-dependent noise.
The performance of the proposed cost function is demonstrated through an
autoencoder with a multi-class classifier attached to the latent space and
trained simultaneously to detect different affective states. An autoencoder
with a state-of-the-art loss function i.e., Mean Squared Error, is used as a
baseline for comparison with our model across four different commonly used
datasets. Centroid and minimum distance between different classes are used as a
metrics to indicate the separation between different classes in the latent
space. An average increase of 14.75% and 17.75% (from benchmark to proposed
loss function) was found for minimum and centroid euclidean distance
respectively over all datasets.