The dimensionality curse has profound e ects on the effectiveness of high-dimensional similarity indexing from the performance perspective. One of the well known techniques for im...
Large numbers of dimensions not only cause clutter in multidimensional visualizations, but also make it difficult for users to navigate the data space. Effective dimension manage...
Jing Yang, Wei Peng, Matthew O. Ward, Elke A. Rund...
High dimensional data visualization is critical to data analysts since it gives a direct view of original data. We present a method to visualize large amount of high dimensional d...
High-dimensional data visualization is receiving increasing interest because of the growing abundance of highdimensional datasets. To understand such datasets, visualization of th...
A model-constrained adaptive sampling methodology is proposed for reduction of large-scale systems with high-dimensional parametric input spaces. Our model reduction method uses a ...