Citadel Quant Research Intern Interview Question
Dimensionality reduction is a fundamental concept in quantitative research, especially in domains like quantitative finance, data science, and machine learning. As datasets become more complex and high-dimensional, managing and analyzing such data efficiently becomes a significant challenge. Understanding dimensionality reduction techniques is crucial for any aspiring quant, particularly when preparing for interviews at top firms like Citadel. In this article, we will explore the various techniques for dimensionality reduction, their mathematical foundations, practical considerations, and their applications in quantitative research.
High-dimensional data is common in quantitative finance and research, where datasets can contain hundreds or thousands of features (variables). While having more features can provide richer information, it also introduces several challenges:
Dimensionality reduction addresses these issues by transforming data into a lower-dimensional space while retaining as much of the relevant information as possible.
