PERSONALIZED ESTIMATION OF DAILY EMOTIONS AND INTERPERSONALCONFLICT BETWEEN ROMANTIC PARTNERS VIA METRIC LEARNING
Abstract
A novel personalized model using metric learning via siamese Neural Network is implemented
to estimate daily emotions and detect interpersonal couples’ conflict using moment-to-moment
multimodal bio-behavior signals (i.e., physiological, linguistic and acoustic signals) and additional
relationship characteristics that includes individuals’ relationship satisfaction and attachment information. The ambulatory couples’ data has high inter-participant variability because each participant have a different distribution of data. Hence, a personalized model that has ability to eliminate the inter-participant variability and preserve the behavior characteristic is likely to perform well. Personalized learning implemented using metric learning via siamese neural network have innate ability to rank the pair of inputs after learning the personalized embeddings. Variants of the proposed personalized model and loss functions have been implemented and explored in this study.
The performance of these proposed models are compared against that of non-personalized models
such as feed-forward neural network.
Subject
metric learningsiamese neural network
moment-to-moment multimodal signals
personalized models
Citation
Venkataramu, Varsha (2021). PERSONALIZED ESTIMATION OF DAILY EMOTIONS AND INTERPERSONALCONFLICT BETWEEN ROMANTIC PARTNERS VIA METRIC LEARNING. Master's thesis, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /196456.