Publications
- Manifold learning: what, how, and whyarXiv preprint arXiv:2311.03757 2023.https://arxiv.org/pdf/2311.03757.pdf
- Skeleton clustering: Dimension-free density-aided clusteringJASA 2023.https://doi.org/10.1080/01621459.2023.2174122
- Helmholtzian Eigenmap: Topological feature discovery & edge flow learning from point cloud dataarXiv preprint arXiv:2103.076262021
- A statistical framework for measuring the temporal stability of human mobility patternsJ. Appl. Stat. 2021
- Refined mode-clustering via the gradient of slopeStats 2021
- Manifold Coordinates with Physical MeaningarXiv preprint arXiv:1811.11891 2021
- The decomposition of the higher-order homology embedding constructed from the k -LaplacianarXiv preprint arXiv:2107.10970 2021
- Tangent Space Least Adaptive ClusteringICML 2021 Workshop on Unsupervised Reinforcement Learning 2021
- The decomposition of the higher-order homology embedding constructed from the k -LaplacianarXiv preprint arXiv:2107.10970 2021
- Water-Accelerated Photo-oxidation of CH3NH3PbI3 Perovskite: Mechanism, rate orders, and rate constants2021
- The EM Perspective of Directional Mean Shift Algorithm2021
- Linear Convergence of the Subspace Constrained Mean Shift Algorithm: From Euclidean to Directional Data2021
- Handbook of mixture analysisJ. Am. Stat. Assoc. 2020
- Kernel smoothing, mean shift, and their learning theory with directional dataarXiv preprint arXiv:2010.13523 2020
- Solution manifold and its statistical applicationsarXiv preprint arXiv:2002.05297 2020
- Measuring human activity spaces from GPS data with density ranking and summary curvesAnn. Appl. Stat. 2020
- Selecting the independent coordinates of manifolds with large aspect ratiosAdvances in Neural Information Processing Systems 2019
- Manifold coordinates with physical meaningNeurIPS 2019 Workshop on Machine Learning for Physical Sciences 2019
- A regression approach for explaining manifold embedding coordinatesarXiv preprint arXiv:1811.11891 2018