4 papers
High-dimensional Semi-supervised Classification via the Fermat Distance
Ruoxu Tan, Yiming Zang
Semi-supervised classification, where unlabeled data are massive but labeled data are limited, often arises in machine learning applications. We address this challenge under high-d…
Semi-supervised Classification for Noisy Functional Data with Application to Astronomical Spectra
Ruoxu Tan, Mingjie Jian, Yiming Zang
Despite its extensive development for multivariate data, semi-supervised learning remains underdeveloped for functional data, especially under discrete and noisy observations. We d…
Supervised Manifold Learning for Functional Data
Ruoxu Tan, Yiming Zang
Classification is a core topic in functional data analysis. A large number of functional classifiers have been proposed in the literature, most of which are based on functional pri…
Delaunay Weighted Two-sample Test for High-dimensional Data by Incorporating Geometric Information
Jiaqi Gu, Ruoxu Tan, Guosheng Yin
Two-sample hypothesis testing is a fundamental problem with various applications, which faces new challenges in the high-dimensional context. To mitigate the issue of the curse of…