ORCID

0000-0002-6164-9714

Keywords

Change Point Detection, Streaming Data, Robust One-Class Classification, Least Squares Support Vector Data Description, Correntropy Loss, Online Learning Algorithms

Abstract

The growing prevalence of high-frequency, high-dimensional data streams in domains such as finance, cybersecurity, and industrial monitoring has intensified the demand for real-time multiple change point detection methods. These methods are expected to identify distributional shifts as they occur while also determining the number and locations of change points—even in the presence of noise, outliers, and limited labeled data. Traditional batch-based approaches and many existing machine learning models fall short in streaming contexts due to their reliance on static datasets and sensitivity to contamination.

This thesis proposes a unified framework for robust multiple change point detection in streaming environments. The framework integrates Least Squares Support Vector Regression (LS-SVR) with Least Squares Support Vector Data Description (LS-SVDD) to identify deviations in residuals that signal distributional changes. To enhance robustness against outliers and noisy data, the standard LS-SVDD is reformulated using a correntropy-based loss function and optimized via the half-quadratic (HQ) technique.

The framework is further extended into an online learning setting, enabling incremental parameter updates and efficient removal of outdated observations without retraining on the full dataset. Extensive simulations and real-world case studies validate the proposed methods, demonstrating superior accuracy, robustness, and computational efficiency compared to existing techniques—providing a practical solution for real-time detection and localization of multiple change points in streaming data.

Completion Date

2025

Semester

Fall

Committee Chair

Maboudou, Edgard

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

Statistics and Data Science

Format

PDF

Identifier

DP0029823

Document Type

Thesis

Campus Location

Orlando (Main) Campus

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