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Lecture 35

Principal Component Analysis


Principal component analysis (PCA) allows you to understand if there are a small number of parts of your data which can explain a wide swath of all data points observed. More specifically, PCA is a common dimensionality reduction technique used in statistics and machine learning to analyze high-dimensional datasets. Principal components allow us to quantify the variability of the data, leading to low-dimensional projections of matrices that contain the bulk of the information contained within the original dataset. It is used in many scientific disciplines and is incredibly applicable across a wide variety of problems. In this lecture, we examine the use of PCA for image processing and for constructing statistical risk factors of a portfolio of securities.

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