Python: Real World Machine Learning

Performing Principal Components Analysis

Principal Components Analysis (PCA) is a dimensionality reduction technique that's used very 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 present in the data. This is where PCA comes into the picture! PCA identifies the important components of the data and arranges them in the order of importance. You can learn more about it at http://dai.fmph.uniba.sk/courses/ml/sl/PCA.pdf. It is used a lot in face recognition ...