Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than ...

Single-cell exploratory analysis methods, including visualization methods and approaches for trajectory estimation, rely on either linear or non-linear data representation, each of which currently ...

Analysis and application of numerical methods for solving large systems of linear equations, which often represent the bottleneck when computing solutions to equations arising in fluid mechanics, ...