Robust Optimization in High-Dimensional Data Space with Support Vector Clustering
Robust Optimization in High-Dimensional Data Space with Support Vector Clustering is a scholarly work, published in 2018 in ''IFAC Proceedings Volumes''. The main subjects of the publication include probabilistic logic, Uncertain data, optimization problem, data mining, computer science, data set, principal component analysis, uncertainty quantification, mathematical optimization, residual, robust optimization, set, mathematics, robust principal component analysis, cluster analysis, algorithm, and subspace topology. The authors propose a data-driven uncertainty set for robust optimization under high-dimensional uncertainty.