PCA Algorithm for Image Dimensionality Reduction

In this article, we employ dimensionality reduction techniques to decrease the dimensionality of images. This approach facilitates the extraction of key features from images, enabling more effective analysis and interpretation. Dimensionality reduction serves as a fundamental data processing technique with widespread applications across various domains, including image processing, pattern recognition, and machine learning. Through dimensionality reduction, we can reduce data complexity, enhance computational efficiency, while preserving essential information. The implementation typically involves calculating covariance matrices, performing eigenvalue decomposition, and selecting principal components based on variance thresholds. In image processing contexts, PCA algorithms transform high-dimensional pixel data into lower-dimensional feature spaces using orthogonal transformations, where the first few principal components often capture the most significant visual patterns. Consequently, dimensionality reduction plays a crucial role in image processing, empowering us to better understand and utilize image data through efficient feature representation and storage optimization.