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University of Cambridge > Talks.cam > Statistics > Distance Shrinkage and Euclidean Embedding via Regularized Kernel Estimation

## Distance Shrinkage and Euclidean Embedding via Regularized Kernel EstimationAdd to your list(s) Download to your calendar using vCal - Ming Yuan (U. of Wisconsin)
- Tuesday 24 May 2016, 14:00-15:00
- MR12, Centre for Mathematical Sciences, Wilberforce Road, Cambridge..
If you have a question about this talk, please contact Quentin Berthet. Although recovering an Euclidean distance matrix from noisy observations is a common problem in practice, how well this could be done remains largely unknown. To fill in this void, we study a simple distance matrix estimate based upon the so-called regularized kernel estimate. We show that such an estimate can be characterized as simply applying a constant amount of shrinkage to all observed pairwise distances. This fact allows us to establish risk bounds for the estimate implying that the true distances can be estimated consistently in an average sense as the number of objects increases. In addition, such a characterization suggests an efficient algorithm to compute the distance matrix estimator, as an alternative to the usual second order cone programming known not to scale well for large problems. This talk is part of the Statistics series. ## This talk is included in these lists:- All CMS events
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