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Found 3 papers, 1 papers with code
Randomized Greedy Algorithms and Composable Coreset for k-Center Clustering with Outliers
Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithm with low complexity for this problem.
Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems
In this paper, we propose a novel robust coreset method for the {\em continuous-and-bounded learning} problems (with outliers) which includes a broad range of popular optimization objectives in machine learning, {\em e. g.,} logistic regression and $ k $-means clustering.
Greedy Strategy Works for $k$-Center Clustering with Outliers and Coreset Construction
Our idea is inspired by the greedy method, Gonzalez's algorithm, for solving the problem of ordinary $k$-center clustering.
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