Abstract：

Manifold learning theory has garnered considerable attention in the modeling of expansive biomedical datasets, showcasing its ability to capture data essence more effectively than traditional linear methodologies. Nevertheless, prevalent algorithms are primarily designed for low-dimensional and clean datasets, whereas contemporary biomedical datasets tend to be high-dimensional and noisy. This presentation addresses the adaptation of these algorithms to effectively accommodate the challenges posed by high dimensionality and noise in modern datasets.