Yi Xu

Title: Innovations of Yi Xu in High-Dimensional Data Analysis

Introduction

Yi Xu is a notable inventor based in Endwell, NY (US), recognized for his contributions to the field of high-dimensional data analysis. He holds two patents that address significant challenges in pattern change discovery between high-dimensional data sets. His work is pivotal in advancing statistical methods and enhancing data analysis techniques.

Latest Patents

Yi Xu's latest patents focus on the general problem of pattern change discovery between high-dimensional data sets. He introduces the concept of principal angles between subspaces to measure the differences between two high-dimensional data sets. Current methods primarily concentrate on magnitude change detection in low-dimensional data sets or operate under supervised frameworks. Principal angles have the unique property of isolating subspace changes from magnitude changes. To tackle the challenge of directly computing these principal angles, he employs matrix factorization as a statistical framework. This approach develops the principle of dominant subspace mapping, allowing the transfer of principal angle-based detection to a matrix factorization problem. Furthermore, matrix factorization can be seamlessly integrated into the likelihood ratio test based on linear models. His method is unsupervised and effectively addresses the statistical significance of pattern changes between high-dimensional data sets.

Career Highlights

Yi Xu is affiliated with the State University of New York, where he continues to contribute to research and innovation in data analysis. His work has garnered attention for its practical applications and theoretical advancements in the field.

Collaborations

Yi Xu collaborates with Zhongfei Zhang, enhancing the scope and impact of their research endeavors.

Conclusion

Yi Xu's innovative work in high-dimensional data analysis exemplifies the importance of statistical methods in understanding complex data patterns. His contributions are significant in the realm of data science and continue to influence future research.