PCA works by finding the directions (principal components) along which the variance in the data is maximized. These directions are orthogonal to each other, ensuring that each principal component captures unique information. The first principal component captures the most variance, followed by the second, and so on. By retaining only the first few principal components, researchers can reduce the dataset's dimensionality while still capturing most of the important information.
