Avhust, Viktor2026-04-142026-04-142026Avhust, Viktor. (2026). DEVELOPMENT OF IMAGE CLASSIFICATION APPROACHES BASED ON DIMENSIONALITY REDUCTION METHODS. Kyiv: American University Kyiv. URI: https://er.auk.edu.ua/handle/234907866/181https://er.auk.edu.ua/handle/234907866/181Principal Component Analysis (PCA) is a powerful tool for reducing the dimensionality of image data while retaining critical information. This capstone project explores the application of PCA in image recognition, focusing on its use in simplifying high-dimensional image datasets to improve recognition efficiency and performance. The study focuses on two primary applications: recognizing handwritten digits (HWD) and classifying galaxies using three different types of classification. Custom software was developed to implement these image classification tasks, integrating dimensionality reduction techniques with classification algorithms to achieve high accuracy rates. The results demonstrate the effectiveness of these approaches in handling high-dimensional datasets, paving the way for using the method and implementing it in human detection software using HWD and tools to support astronomers in their research.en-USPrincipal Component Analysis (PCA)Dimensionality ReductionComputer VisionHandwritten Digit RecognitionMachine LearningThesis