Automatic Prediction of Friendship via Multi-model Dyadic Features
Yu Zhou, David Gerritsen, Amy Ogan, Alan W. Black, Justine Cassell · 2013
In this paper we focus on modeling friendships between humans as a way of working towards technology that can initiate and sustain a lifelong relationship with users. We do this by predicting friendship status in a dyad using a set of automatically harvested verbal and nonverbal features from videos of the interaction of students in a peer tutoring study. We propose a new computational model used to model friendship status in our data, based on a group sparse model (GSM) with L2,1 norm which is designed to accommodate the sparse and noisy properties of the multi-channel features. Our GSM model