All Lectures
Lecture 36

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.

The lectures on this website are provided for informational purposes only and do not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor do they constitute an offer to provide investment advisory services by Quantopian.

In addition, the lectures offer no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.