Samuel Veer Singh
Title: Cluster Analysis of Multi-Variate Functional Data through Non-linear Representation Learning
Supervision Team: Mimi Zhang, TCD / Shirley Coyle, DCU
Description: Wearable sensors provide a continuous and unobtrusive way to monitor an individual’s health and well-being in their daily lives. However, interpreting and analyzing wearable sensor data is challenging. One important technique for analyzing such data is cluster analysis. Cluster analysis is a type of unsupervised machine learning that involves grouping data points into clusters based on their similarities. In the context of wearable sensor data, this can involve grouping together measurements of physiological parameters such as heart rate, respiratory rate, and activity level, as well as environmental data such as temperature and humidity. This project involves working on the cutting-edge of cluster analysis methods for sensor data. Different from traditional machine learning methods (for multivariate data), we will develop functional data clustering methods, motivated by the fact that sensor data can be naturally modelled by curves that denote continuous functions of time.