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Name: Vladimir Fomichov
Talk Title: Spherical clustering in detection of groups of concomitant extremes
Abstract: There is growing empirical evidence that spherical k-means clustering performs well in identification of groups of concomitant extremes in high dimensions, thereby leading to sparse models.
We provide first theoretical results supporting this approach, but also identify some pitfalls. Furthermore, we show that an alternative cost function may be more appropriate for identification of concomitant extremes, and it results in a novel spherical k-principal-components clustering algorithm. Our main result establishes a broadly satisfied sufficient condition guaranteeing the success of this method, albeit in a rather basic setting.
Finally, we illustrate in simulations that k-principal-components outperforms k-means in the difficult case of weak asymptotic dependence within the groups.
This talk is an invited talk at EVA 2021. View the programme here.