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Variable clustering: optimal bounds and a convex approach

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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.

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