Search Results for author: Didong Li

Found 13 papers, 8 papers with code

STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics

1 code implementation10 Jun 2024 Jiawen Chen, Muqing Zhou, Wenrong Wu, Jinwei Zhang, Yun Li, Didong Li

Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology.

Contrastive inverse regression for dimension reduction

no code implementations20 May 2023 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 functional relation with certain response variables of interest.

Dimensionality Reduction regression

Kernel Density Bayesian Inverse Reinforcement Learning

1 code implementation13 Mar 2023 Aishwarya Mandyam, Didong Li, Diana Cai, Andrew Jones, Barbara E. Engelhardt

Inverse reinforcement learning~(IRL) is a powerful framework to infer an agent's reward function by observing its behavior, but IRL algorithms that learn point estimates of the reward function can be misleading because there may be several functions that describe an agent's behavior equally well.

BIRL Density Estimation +2

Spherical Rotation Dimension Reduction with Geometric Loss Functions

1 code implementation23 Apr 2022 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.

Dimensionality Reduction

From the Greene--Wu Convolution to Gradient Estimation over Riemannian Manifolds

no code implementations17 Aug 2021 Tianyu Wang, Yifeng Huang, Didong Li

Over a complete Riemannian manifold of finite dimension, Greene and Wu introduced a convolution, known as Greene-Wu (GW) convolution.

Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering

no code implementations17 Aug 2020 Debolina Paul, Saptarshi Chakraborty, Didong Li, David Dunson

In a rich variety of real data clustering applications, PEA is shown to do as well as k-means for simple datasets, while dramatically improving performance in more complex settings.

Clustering Computational Efficiency +1

Estimating densities with nonlinear support using Fisher-Gaussian kernels

1 code implementation12 Jul 2019 Minerva Mukhopadhyay, Didong Li, David B Dunson

We provide theory on large support, and illustrate gains relative to competitors in simulated and real data applications.

Methodology Statistics Theory Statistics Theory

Geodesic Distance Estimation with Spherelets

no code implementations29 Jun 2019 Didong Li, David B. Dunson

When the manifold is unknown, it is challenging to accurately approximate the geodesic distance.

Clustering Density Estimation

Efficient Weingarten Map and Curvature Estimation on Manifolds

1 code implementation26 May 2019 Yueqi Cao, Didong Li, Huafei Sun, Amir H Assadi, Shiqiang Zhang

In this paper, we propose an efficient method to estimate the Weingarten map for point cloud data sampled from manifold embedded in Euclidean space.

Classification via local manifold approximation

1 code implementation3 Mar 2019 Didong Li, David B. Dunson

It is challenging to obtain accurate classification performance when the feature distributions in the different classes are complex, with nonlinear, overlapping and intersecting supports.

Classification General Classification +1

Efficient Manifold and Subspace Approximations with Spherelets

3 code implementations26 Jun 2017 Didong Li, Minerva Mukhopadhyay, David B. Dunson

There is a rich literature on approximating the unknown manifold, and on exploiting such approximations in clustering, data compression, and prediction.

Clustering Data Compression +2

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