In financial portfolios, the returns on their constituent assets depend on a number of factors, such as macroeconomic and microeconomical conditions, and various financial variables. As the number of factors increases, so does the complexity involved in modeling portfolio behavior. Given that computing resources are finite, coupled with time constraints, performing an extra computation for a new factor only increases the bottleneck on portfolio modeling calculations. A linear technique for dimensionality reduction is Principal Component Analysis (PCA). As its name suggests, PCA breaks down the movement of portfolio asset prices into its principal components, or common factors, for further statistical analysis. Common factors that don't explain much of the movement of the portfolio assets receive less weighting in their factors and...