This study investigates the role of feature extraction and dimensionality reduction techniques in addressing high-dimensional image data, with a particular focus on Alzheimer's disease classification using 2D magnetic resonance imaging (MRI). Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are employed to extract discriminative features from MRI images; however, due to the high dimensionality of the extracted features, dimensionality reduction is required. Principal Component Analysis (PCA) is utilized to reduce feature dimensionality while preserving most of the relevant information, as reflected in the improved performance of the underlying machine learning (ML) class... More >