Xi He

Quantum locally linear embedding for nonlinear dimensionality reduction

2019 · arXiv (Cornell University)

Reducing the dimension of nonlinear data is crucial in data processing and visualization. The locally linear embedding algorithm (LLE) is specifically a representative nonlinear dimensionality reduction method with well maintaining the original manifold structure. In …

Computing Inconsistency Measures Under Differential Privacy

2025 · Proceedings of the ACM on Management of Data

Assessing data quality is crucial to knowing whether and how to use the data for different purposes. Specifically, given a collection of integrity constraints, various ways have been proposed to quantify the inconsistency of a …