Clustering and feature detection methods for high-dimensional data

Abstract

The majority of the real-world data are unlabeled. Moreover, complex characteristics such as high-dimensionality and high variety pose significant analytical challenges. In statistical and machine learning, supervised and unsupervised methods are used to analyze labeled and unlabeled data, respectively. Compared to supervised learning methods, unsupervised learning is less developed. Therefore, this dissertation focuses on developing unsupervised methods to perform clustering and feature detection tasks in real-world high-dimensional data settings. Specifically, we develop methods to cluster censored spatio-temporal data, detect pixel-level features in medical imaging data, and adaptively detect anomalies in industrial optical inspection images and candidates’ emotions in interview videos. The overarching objective of these methods is to help stakeholders improve the performance of the associated systems in terms of user engagement, patient comfort, customer satisfaction, and product quality.