MATLAB Implementation of PCA Dimensionality Reduction Method for Image Processing

In image processing, the PCA (Principal Component Analysis) dimensionality reduction method serves as a widely-used technique. PCA dimensionality reduction helps reduce the dimensionality of image data while preserving most of the critical information. When dealing with large volumes of image data, we need to consider how to reduce data dimensionality to facilitate better analysis and processing. Using the PCA dimensionality reduction method, we can transform image data from high-dimensional space to low-dimensional space, which aids in better understanding and analyzing images. The MATLAB implementation typically involves several key steps: first standardizing the image data using z-score normalization, then computing the covariance matrix to identify data variance patterns. The eigen decomposition of the covariance matrix helps determine principal components, where selecting the top k eigenvalues corresponding to eigenvectors effectively reduces dimensionality. This processing method can be applied to various image processing tasks such as face recognition and image classification. In MATLAB, key functions like pca() or eig() can be utilized for implementation, with additional preprocessing steps often including image vectorization and data normalization to ensure optimal PCA performance.