Graduation Semester and Year
Fall 2024
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Electrical Engineering
Department
Electrical Engineering
First Advisor
Dr. Ioannis Schizas
Second Advisor
Dr. Michael Manry
Third Advisor
Dr. Qilian Liang
Fourth Advisor
Dr. Jonathan Bredow
Abstract
The increasing demand for real-time analysis of sensor data in dynamic environments necessitates innovative approaches to data clustering. This work introduces a novel Online Kernel Clustering (OKC) framework that efficiently determines time-varying clustering configurations without requiring training data. The proposed method employs sparse kernel factorization, guided by a time-dependent metric to quantify the closeness of kernel similarity matrices to a block diagonal structure. By processing data sequentially, the OKC framework is tailored for non-stationary settings. The optimization process integrates block coordinate descent, difference-of-convex functions minimization, and projected sub-gradient descent to iteratively update kernel covariance matrices and cluster memberships online. Numerical experiments using synthetic and real-world datasets confirm the superior clustering accuracy and reduced runtime of the OKC framework compared to existing methods. Additionally, a function approximating Q-learning approach enables an agent to iteratively refine kernel parameters, leveraging a state-action framework to optimize clustering outcomes dynamically. The integration of neural networks for Q-function approximation facilitates scalability and robustness in high-dimensional state-action spaces. The reinforcement learning component demonstrates adaptability to dynamic configurations, reducing computational overhead while maintaining high performance. This research contributes a robust, scalable solution for unsupervised clustering in evolving environments, offering practical applications in fields such as activity recognition and hyperspectral imaging.
Keywords
Kernel Clustering, Reinforcement Learning, Function Approximation, Neural Networks
Disciplines
Signal Processing
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Alam, Abrar U., "Unsupervised Machine Learning in Wireless Sensor Measurements and Hyperspectral Imaging" (2024). Electrical Engineering Dissertations. 404.
https://mavmatrix.uta.edu/electricaleng_dissertations/404