Christian Zuniga, PhD

Principal component analysis (PCA) is an unsupervised, linear technique for dimensionality reduction first developed by Pearson in 1901 [1],[2],[3]. It is widely used in many areas of data mining such as visualization, image processing and anomaly detection. It is based on the fact that data may have redundancies in its representation. Data refers to a collection of similar objects and their features. An object could be a house and the features the location, the number of bedrooms, the square footage, and any other characteristic that can be recorded of the house. In PCA…