13 citations · 72 across the 18 of their papers we have counts for
5 papers · 1 filter
Contrastive inverse regression for dimension reduction
Sam Hawke, Hengrui Luo, Didong Li
Supervised dimension reduction (SDR) has been a topic of growing interest in data science, as it enables the reduction of high-dimensional covariates while preserving the functiona…
Spherical Rotation Dimension Reduction with Geometric Loss Functions
Hengrui Luo, Jeremy E. Purvis, Didong Li
Modern datasets often exhibit high dimensionality, yet the data reside in low-dimensional manifolds that can reveal underlying geometric structures critical for data analysis. A pr…
Efficient Weingarten Map and Curvature Estimation on Manifolds
Yueqi Cao, Didong Li, Huafei Sun +2
In this paper, we propose an efficient method to estimate the Weingarten map for point cloud data sampled from manifold embedded in Euclidean space. A statistical model is establis…
Classification via local manifold approximation
Didong Li, David B Dunson
Classifiers label data as belonging to one of a set of groups based on input features. It is challenging to obtain accurate classification performance when the feature distribution…
Efficient Manifold and Subspace Approximations with Spherelets
Didong Li, Minerva Mukhopadhyay, David B. Dunson
In statistical dimensionality reduction, it is common to rely on the assumption that high dimensional data tend to concentrate near a lower dimensional manifold. There is a rich li…