11 citations · 19 across the 5 of their papers we have counts for
10 papers · 1 filter
A Sub-linear Time Framework for Geometric Optimization with Outliers in High Dimensions
Hu Ding
Many real-world problems can be formulated as geometric optimization problems in high dimensions, especially in the fields of machine learning and data mining. Moreover, we often n…
Layered Sampling for Robust Optimization Problems
Hu Ding, Zixiu Wang
In real world, our datasets often contain outliers. Moreover, the outliers can seriously affect the final machine learning result. Most existing algorithms for handling outliers ta…
A Data-Dependent Algorithm for Querying Earth Mover's Distance with Low Doubling Dimensions
Hu Ding, Tan Chen, Fan Yang +1
In this paper, we consider the following query problem: given two weighted point sets and in the Euclidean space , we want to quickly determine that whether t…
The Effectiveness of Johnson-Lindenstrauss Transform for High Dimensional Optimization With Adversarial Outliers, and the Recovery
Hu Ding, Ruizhe Qin, Jiawei Huang
In this paper, we consider robust optimization problems in high dimensions. Because a real-world dataset may contain significant noise or even specially crafted samples from some a…
Minimum Enclosing Ball Revisited: Stability and Sub-linear Time Algorithms
Hu Ding
In this paper, we revisit the Minimum Enclosing Ball (MEB) problem and its robust version, MEB with outliers, in Euclidean space . Though the problem has been extensi…
Greedy Strategy Works for -Center Clustering with Outliers and Coreset Construction
Hu Ding, Haikuo Yu, Zixiu Wang
We study the problem of -center clustering with outliers in arbitrary metrics and Euclidean space. Though a number of methods have been developed in the past decades, it is stil…