University of Cambridge > Talks.cam > Statistics > Variable clustering: optimal bounds and a convex approach

Variable clustering: optimal bounds and a convex approach

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Quentin Berthet.

The problem of variable clustering is that of grouping similar components of a p-dimensional vector X = (X_1 , ... , X_p), and estimating these groups from n independent copies of X. Although K-means is a natural strategy for this problem, I will explain why it cannot lead to perfect cluster recovery. Then, I will introduce a correction that can be viewed as a penalized convex relaxation of K-means. The clusters estimated by this method are shown to recover the partition G at a minimax optimal cluster separation rate.

This talk is part of the Statistics series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2024 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity