Cooperating with the Curse of Dimensionality
Seminar: CS: Data Science | April 25 | 4-5 p.m. | 3106 Etcheverry Hall
Hao Chen, UC Davis
The curse of dimensionality arises when analyzing high-dimensional data and non-Euclidean data, such as network data, which are ubiquitous nowadays. It causes counter-intuitive phenomena and makes traditional statistical tools less effective or inapplicable. On the other hand, some counter-intuitive phenomena might be explained by some universal patterns, which could be used to form new effective tools in dealing with high-dimensional/non-Euclidean data. In this talk, one such unique pattern is explored and applied to fundamental statistical tasks, including hypothesis testing and cluster analysis, leading to substantial improvements in conducting these tasks for high-dimensional/non-Euclidean data. Some other related topics will also be briefly discussed.