Seminar at SMU Delhi
January 27, 2017 (Friday) ,
4:30 PM at Webinar
Estimating sparse eigenstructure for high dimensional data
Abstract of Talk
When data is high dimensional, widely used multivariate methods such as principal component analysis can behave in unexpected ways. Upward bias in sample eigenvalues and inconsistency of sample eigenvectors are among the new phenomena that appear. In recent years there has been much progress on exploiting sparsity to respond to these phenomena. The talk will give an overview of this area.