PCA-Based Unsupervised FE

Principal component analysis (PCA) is generally considered to be a tool to visualize the relationship between sample objects as a statistical tool especially when the number of features attributed to individual samples is too huge to interpret. Mathematically, PCA is nothing but a linear projection of objects in high dimensional space onto low dimensional space. Alternatively, PC can be considered to be a tool that performs feature extraction (FE), because principal components (PC) that PCA generates can be used as new features attributed to individual objects. In this chapter, I would like to add one more function to PCA, feature selection. I demonstrate how we can make use of PCA in order to select features and how well it works in which situations. This can be also a good introduction for TD-based unsupervised FE, which is in some sense the extension of the method proposed in this chapter.