Principal component analysis (PCA) is a dimensionality reduction technique that's used frequently in computer vision and machine learning. When we deal with features with large dimensionalities, training a machine learning system becomes prohibitively expensive. Therefore, we need to reduce the dimensionality of the data before we can train a system. However, when we reduce the dimensionality, we don't want to lose the information that's present in the data. This is where PCA comes into the picture! PCA identifies the important components of the data and arranges them in order of importance.
Performing principal component analysis
Getting ready
In this recipe, we will see how we can perform PCA on input data...
